{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bikeshare数据集上的数据探索\n",
    "在Capital Bikeshare （美国Washington, D.C.的一个共享单车公司）提供的自行车数据上进行回归分析。根据每天的天气信息，预测该天的单车共享骑行量。\n",
    "字段说明 Instant：记录号， Dteday：日期， Season：季节（1=春天、2=夏天、3=秋天、4=冬天）， yr：年份，(0: 2011, 1:2012)， mnth：月份( 1 to 12) ， holiday：是否是节假日， weekday：星期中的哪天，取值为0～6， workingday：是否工作日 1=工作日 （是否为工作日，1为工作日，0为非周末或节假日）， weathersit：天气（1：晴天，多云 ",
    "2：雾天，阴天 ",
    "3：小雪，小雨 ",
    "4：大雨，大雪，大雾）， temp：气温摄氏度， atemp：体感温度， hum：湿度 ，windspeed：风速， casual：非注册用户个数， registered：注册用户个数 ，cnt：给定日期（天）时间（每小时），总租车人数，响应变量y （cnt = casual + registered）\n",
    "\n",
    "casual、registered和cnt三个特征均为要预测的y，作业里只需对cnt进行预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数据读取及基本处理\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# plotting\n",
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# setting params\n",
    "params = {'legend.fontsize': 'x-large',\n",
    "          'figure.figsize': (30, 10),\n",
    "          'axes.labelsize': 'x-large',\n",
    "          'axes.titlesize':'x-large',\n",
    "          'xtick.labelsize':'x-large',\n",
    "          'ytick.labelsize':'x-large'}\n",
    "\n",
    "sn.set_style('whitegrid')\n",
    "sn.set_context('talk')\n",
    "\n",
    "plt.rcParams.update(params)\n",
    "pd.options.display.max_colwidth = 600\n",
    "\n",
    "# pandas display data frames as tables\n",
    "from IPython.display import display, HTML\n",
    "# 读入单车数据\n",
    "train = pd.read_csv(\"day.csv\")\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.5+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000000000ACEDCF8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000AE6B630>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000000000AE970F0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000AEBCB70>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#直方图查看数值型特征分布\n",
    "numerical_features = ['temp','atemp','hum','windspeed']\n",
    "train[numerical_features].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xaf33198>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#年份和骑行量之间的相关性\n",
    "sn.violinplot(data=train[['yr',\n",
    "                        'cnt']],\n",
    "              x=\"yr\",y=\"cnt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb063208>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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X6+W3337ju+++O+4BJtXuYUjLSi5v4qq3pSe9kSRon+ihfWIJO4pNfLE3hqzDFgoKCpg+fTrvv/8+V155Jd27dyc2NlZvcwNGMAWjkpMLgxU4TgBkWU5A2xAfrijK0qqxkUA2MAp4r7aThnP0TUlJCQsXLuTzzz+npKQEqFp6atQKV+qFkSEUJ2IwHS8cBZswuMrZuHEjGzdupHPnzlx33XV06tQpqjcf9aSiooKvv/6azz777Lgifm0auBncopJOye56z9AOJdo08PC3i0rJKTPyVbaNVQeslJeXs2TJEr777jv69evHtddeS7Nm9VxFIUic7iE7mIKRC8TKshyvKEop+IXBDuw/4dh2aHsbfv+2avlqO5rnUWvC0U08cOAAc+fOZdGiRf4lNVUy4E46D1dqx8BlaIcSfuFoh+nILiz5GzE6ilm/fj3r169HlmXGjBlDjx49MBqNZ34/wTlTWFjI/Pnz+eKLLygvP1ZX7OJkF4NbVNK2YXS1Qk2P8zKhfTkjMitZkmNjZZ4Nh9PJwoULWbRoET169GD06NGcf/75eptabwRTMLKAcqAH8FXVWK+qsawTjq32by8Cqj0MG5pY7Aq4pTqxe/duPv74Y5YvX+6vu6QaTLhTZFxNOqBa43S2UAcMVXscSa0xHc3GUrARY3khiqLw9NNPk5aWxvXXX0///v1FZm6A2LdvH//73/9YunSpvz+22aDSvamTAemVNIuN7qZEyTYff2lTwbCMSr7db2VZbgxFLli5ciUrV67koosuYsyYMXTt2jXsveJgJ+79CxgK3AxIwAdoobMPy7LcALAoinKo6tjPABm4A20P40ngcqB9bfcwwiGsVlVVsrKy+Pjjj/31dAB8JhvuJu1xNT4fTPrdCAMWVnu2qCrG0gIsBRsxFR9zTBMTExkxYgRDhw4lPj4MosTCgC1btjBnzhx++ukn/1isycefmjvo29xBA4v+/U4CGVZ7trh98EuBlcU5NvaXH3smz8jI4IYbbqBPnz4hHVkVKmG1AI8AMcBCwAPMBh6v+t1/gKuAjKqfb0RL9PsIbXnqB+Cqumx4hzI+n49ffvmF2bNn8/vvvx8bt8bjanoB7uQ2YAjdi0o3JAlvQiqVCakYKg5jyd+E6cgejh49yttvv83s2bMZMmQIo0aNIikpAvd4Akx1xNPs2bP95TtAy6EYkO6gZ6oDm7gsT4vZAD2bObky1UnWYTNfZcegFJnZu3cvU6dO5d1332X06NEMGDAg7LzioHoYwSYUPQyPx8OKFSuYPXv2cZVbvfYkXE074mmUETqZ2YSgh3ESJGcploLNmAu3I1U1eTKbzQwcOJDRo0eH7eZjMFFVldWrV/Pee++xdetW/3jzWA9Xt6yka+PQjHgKRQ/jZOwqNvHlvhjWFR6r5ZaUlMQNN9zA1VdfHVLCcToPQwhGkPB4PCxdupQPP/zwuBBET3wqrtQLtb4UIbi+GQ6CUY3krsR8cCuWA78jebXyZAaDgT//+c/cdNNNNG9+snQfwYYNG3jnnXf8jYsAWie4GZJRyUVJoR3xFC6CUU1umZEv98Ww6qDFX68qJSWFG2+8kYEDB2I2m8/wDoFHCIaOeDweli1bxqxZs45PtmvYQguNjWuso3VnJpwEw4/XjfngNiwHNmNwaxVnDAYDffv25cYbbxTCUcXu3buZPn36cc2D2jRwM7xVBe0TPaH4/PIHwk0wqjlYaeDzvTH8VHCs0GFqaioTJ06kZ8+eum6Oh9IeRtSgqirfffcdM2fOPCErO6Mq2S4Ms7LDBaMZd2pH3E3Or0oC3AjuCpYsWcKyZcsYMGAAt956K8nJyXpbqgtFRUW8++67LFy40F/nqVW8hxGZFXRsFFoFASOVxjE+bj+/nGtaVvLZHju/HLCQn5/PU089xUUXXcSkSZNo27at3mb+ASEYAWDjxo288cYbx60FC6HQgROTAKuE46uvvmL58uWMGjWKMWPGYLfb9bY0KKiqypIlS5g2bRplZWWAVr5j9HkVXJISnMZFguNpavcxsUMZV7c0MmeHnc1HLWRlZTFhwgSGDx/ObbfdFlKNxoRg1COHDh3itdde4/vvj9VX9DRojrP5JUIo9KSmcBzchjUvC6fTyaxZs/jyyy+588476du3b9jHyJ+OgwcP8vLLL/Prr78CYDP6GJpRSd/mDiwi71F3msd5ebBTKVmHzczZGUtBhZH58+fz888/89BDD3HxxRfrbSIgenrXC16vl08//ZSbb77ZLxZeexIV8gAq2/YTYhEqGEy4m15A2YUjcTXtiCoZOXr0KFOnTuVvf/sbubm5elsYEFavXs348eP9YtEpycULlxczuKUQi1BCkqBTspuplxUxLKMCo6SSn5/P/fffz7vvvutfPtQT4WGcI/n5+UyZMsW//KQarTjTL9XyKCL4iTWsMWnnyNW4HbZ9qzAV57B+/XpuueUWJkyYwIgRIyLC21BVlTlz5vD222+jqiqxJh83ti3niiZi+SmUMRlgeGYll6S4mLk1jn1lJt5//30UReHxxx/XNTFVeBjnwPfff89tt93mFwt30nmUdxyOO6WtEIswQLXGU9nmz1S27oPPbMftdjNt2jT+/ve/U1paqrd554Sqqrz66qvMnDkTVVVpEedhyqXFdGsqxCJcaBHv5e9diumVqtWTW7VqFQ888IC/EKkeCME4C1RV5e233+bJJ5+kvLwcn8lKRZu+ODJ7oppDZ4NKUAskCU+jDMovGI67USsAfvzxR26//fawXaJSVZVp06axYMECAC5r7OTvXYpJidF/SUNQNyxGuLVdOTe3LUNCZceOHUyePFm3BxohGHXE5/Mxbdo0PvzwQwA8cY2p6DAMb0PRpyGsMVlwZF6Fo2U3VMlIQUEB99xzD3v27NHbsjqzYMEC5s+fD0C3Jk7u6lCGVexVhC2SBH9q7uSO9ppobN++nWeffVaXPQ0hGHXkjTfe8H8Y3Y1aUSkPQrVEbsOUqEKScDduR6XcH9Vg5siRI9x3333+NrjhwPbt23njjTcA6JLs4o72ZSGdqS2oPd2buhgna2XlV69ezccffxx0G4Rg1IEff/yRTz75BND2KxyZvSBCG8FHM974plTIA1CNFoqLi/nHP/7hL+sdyqiqyksvvYTb7SbF5uV2IRYRR+80J92bansa//3vf8nPzw/q/OJuV0uKiop48cUXAW0ZytHqypAqEiioX3xxKVRm9gJg69atzJo1S2eLzszKlSvZvn07AOPPL8NuityyP9HMzW3LaWDx4fF4eO+994I6t7jj1ZKvvvqKkpISVIMJR+ZVQiyiAG/DdK0fCfDpp5/idJ6sJX3oMHfuXAA6NnLRPjH0PSLB2WEzwZAMrUbasmXLOHLkSNDmFne9WqCqKl9++SWgLUVFZee7KMWVeiEqEqWlpaxcuVJvc05Jbm6uv69Kv3SHztYIAk3PVAc2o4rP52P58uVBm1cIRi0oLi72FxD0JNWppbggzFEtsXjjmwIc1+gq1Pj5558BiDf7uCDRrbM1gkBjNUKXFM3jrT73wUAIRi04fPiw/3ufVbT/jDZ8VR5lMF3/upKVlQVAh0R3SDY6EtQ/FzTSHgy2bNmC2x2chwRxadWVqo5u0YKh4thN0pr9K4ayQzpaow+SGvoJb4qiANCmodi7iBbaNtDOtcvlClq+kBCMWpCeno7RqGU+GStD9ymzvjGUHSJmz7HKu+biHOzK11EnGtWi2apVK50tOTklJSUUFhYC0CIuugQjp+xYRuLsHbHsLomeDMVkmw+bUXuYEYIRQlgsFtq0aQOA6Uj4Zf6eLZYDW5B8x9+AJJ8by4EtOlkUfAyVRzFWHgWgffv2OltzcnJycvzfN7NHjwe8u8TIm1uPLRH/dtjCCxsSokY0JAnSYrXzXfMaCCRCMGrJ4MGDATAd3YvkLNPZmuBgLDtQp/FIxHxA2+hu3Lgxerf7PRXVmegxRh9x5ujJvfg6Jwan9/jMRIfXwJKc6Knn1riqPljNrp6BRAhGLfnzn/9MQkICkqpizV2jtznB4VT7NVGyj2OoOIL5kJYId+2112IyhWY3gIKCAgBSYnxRVYl2e9HJz4dyivFIJCVG+ywGK+NbCEYtiYmJ4Y477gDAfGQPxuLwrGQqqCWqD9u+n5FQadasGcOHD9fbolPiFwxb6G/O1ydu38nV8VTjkUj1Oa++BgKNEIw6MGjQIM4/X8v8te3+AcldqbNFgkBhycvCWHYQgHvuuQer1aqzRafmwAFtiTDJFh2en+AYSVWCcfTo0aBUIhCCUQcMBgOPP/44MTExGDyV2HZ/D2EQcimoG8aSfCx5vwEwZMgQLr/8cp0tOj3HBENci9FGco2HhIMHDwZ8PiEYdaR58+bcf//9AJhK9mPZv15niwT1ieQsw7ZrBRIqrVq1YtKkSXqbdFpUVeXQIS3MWQhG9NHIeuycV18HgSSou0OyLJuAl4EbquaeBUxWFMV1iuPvB+4DkoAfgImKouwLkrmnpF+/fmzevJkvvvgCa/5GfPZkPI0y9DZLcK74PMTs+haDx0FsbCxTpkwJ6aUogLKyMv9SRKJVCEa0YTFCrMlHucfgz8UJJMH2MKYCA4EhwLCqf6ee7EBZlicCzwIPAJehCcwnwTHzzNx9993+uHzbnu8xVMXqC8IUVcW27xeM5dqH7rHHHiM9PfS7KBYXF/u/TzALwYhGEizaea95LQSKoAmGLMs24C7gb4qi/KIoykrgr8BEWZZjTjhWAp4AnlAUZb6iKL8DdwJNZVlOC5bNp8NisTBlyhQaNWqE5PMQs2M5eE/qKAnCAPMhBXPhDgBuvvlmunfvrrNFtaOkpMT/fWwU5WAIjlGde1PzWggUwfQwOgGxwPc1xlZWjXU64dj2QBo1PApFUXYqitJCUZSQ6ZeZnJzMlClTMJlMGJwl2Pb+BKr40IYbhvLDWLN/BaBbt27cfPPNOltUe2oWnbMYxLUXjZir7uLBKEAYzD2MNKBcURS/36QoSoksyxVA8xOOPQ9wApmyLM8DMoCfgb8qilKnlEaHI7C9Ac477zzGjx/Pm2++ifnIHrwJzXCnyAGdU1CPeN3E7FqBpHpp0qQJ999/Py5X+HiKNa/vaEraExxDQntQcLlcAb/fBVMw7GgicCJO4MSdxXg072cm8CBwEHgO+FqW5c6KotS6wtqWLYGve9SmTRs6dOjAli1bsGavxpOQJposhQnW/eswOEswGAyMHj2a7OxsvU2qEzUzfB0eCbNFeBnRhqOqPEpZWVnA73fBFIxK/igMVI1VnDDmBszAw4qiLASQZXkMUABcBXxT20k7dOhwNrbWmSeeeIIJEyZQUlKCbd/PVLbtF5R5BWePoeygv1bU6NGjGThwoM4W1Z2kpCT/98UuA/EWkbwXbRS7tDWp1q1b18v97nSiE0zByAViZVmOVxSlFECW5QQ0z+PEfYnqZafN1QOKohySZbkQaFmXSW0229lbXAdSU1O5++67mTp1KqbiXIwleXgTmgVlbsFZoKpYc9YgARkZGdxyyy2YzWa9raoz6enpmM1m3G43eRVGmscJwYgmnF4odGjVeTMzMwN+vwvmpncWUA70qDHWq2os64RjN6B5JP7yoLIsNwaSgd2BNfPs6du3L+3atQPAmrtWbICHMMbiXExVVXcnTpwYlmIBYDQa/X06dhZHT9E9gcauGuc8MzPw7aODJhiKolSi7UlMk2W5hyzLPYHXgNcVRXHIstxAluWUqmPLgOnAK7Is95VluQPwIbCF46OsQgpJkrj99tsBMJYXYiiPrkZD4YSlaimqY8eOdO3aVWdrzo3qsuubjoSn6AnOno1HLIDmJddcngwUwU7cewRYCiwEPgU+Bx6v+t1/gJp1wx8G3kfLBl8NOICBiqKEtM/duXNnMjIyALAcUvQ1RnBSJGcpphJtFXTEiBFIYR5eVF3ran+5ib2l0dE8SAA+FVYd0AQjWPXOgurDKoriBCZWfZ34u3En/OxFE5PHTzw2lJEkiYEDB/LGG29gLMrRlqXC/IYUaZiKtO5k8fHxXHnllTpbc+507NiRZs2akZeXx4r9Nm5pV663SYIgkHXYzBGn9oAwYMCAoMwpig8GgOolDoPHgaHisM5r1YpvAAAgAElEQVTWCE7EVKx5F5dccknINkWqCwaDgSFDhgDwY4GVIqd4QIl0VBW+3KcVyLjwwgv9qxqBRghGAGjZsiUJCQmA1rVNEFpUn5OOHTvqbEn9MWTIEBISEnD7JL7YFz0tSqOVTUfM7CjW9qxuvPHGoM0rBCMASJJEixYtADA4Al8QTFAHvG4Mbm3JpmXLOkVohzR2u53rr78egG/328grFx/tSMXrg4922gEtz+ySSy4J2tziqgoQKSkpgLYsJQgdpBrnIzk5WUdL6p+RI0fSpEkTfKrE7B2xIqo7QlmRZ2V/ubaUOmnSpKAGbQjBCBAxMVXLAt7AFwQT1B7Je6yqjP8cRQhWq5UJEyYAsOmIhbWHLDpbJKhvipwS83Zr3kXfvn39LRaChRAMgSCC6N27tz8v48MddiprXXVNEA58vDOWCo+B2NhYJk78Q7BpwBGCESAqKyu1bwzhH4UTSajGY+cj0JU99UCSJO677z7MZjNHnUYW7LHrbZKgnvj9iImfD2jl+MaPHx+URL0TqfXdTJblhsD1wAWAF62cx1xFUUTQ90koKioCQDWFdovPaKPm+SgqKvIHJ0QS6enpjB49mlmzZrE010avZk7SYkM631VwBrw+mLUjFoC2bdsydOhQXeyolYchy/LFwA7gaaAt0AF4Adgmy3LbgFkXxhw8eBAAn0WUOQ8pjBZUoxaOeODAAZ2NCRxjx471b4DP2RG5XobBYKBr165cd911dO3aFYMhMhdNvs2z+Te677vvPoxGfTL6a/u/+zrwJdBSUZT+iqL0Rasau6Lqd4IauFwuf58Cny1eZ2sEJ+Kzajkyubm5OlsSOKxWK3feeSegbYBvPRqZS6OXXnopL7zwApMmTeKFF14IaohpsHB64bM9WoBGv379gr7RXZPaCkYn4AVFUfytyBRFcaA1NboiEIaFMzk5Ofh8WmN2X0yiztYITsQX0xCA3btDtvBxvdCrVy9kWev++GmE7mW0aNHCH1YqSVJE5dZU802ujVK3AZPJxPjx43W1pbaC8Rta46IT6QxsrTdrIoRt27YBoBrNqGJJKuTw2hsBoCiRXRxSkiTGjRsHgFJkjsjChNnZ2ahVCSeqqrJv3z6dLapfvD5Ylqv1uBg4cCBNmjTR1Z7a+qmfAC/JstwF+BHwoInFBOAdWZbvqj5QUZTp9W5lmLF1q6ah3tgUUXgwBPHFakmVBw8epLCwMOIS+GrStWtX0tLS2L9/f0QWJlyzZg0PP/wwLVu2ZN++faxdu5bYCNLFjUeOFRgcOXKkztbUXjDuAQqBvlVf1RQCNbfrVbQ+FlFNVpbWD8ob11hnSwQnwxubjCoZkFQfGzdupE+fPnqbFDAMBgODBg1i5syZrD1k4Wa5HEMEPcP4fD5Wr17N6tWrjw1GkGCsPaglX15wwQUhsdxWK8FQFKVVoA2JFA4fPkxOjlY+2xufqrM1gpNiMOGNa4yptIANGzZEtGAAdOvWjZkzZ1LqNrCv1EirBBFiGw6oqhawANC9e3edrdGoSx6GHWgFnJhYoCqKsqFerQpj1qzRekCpBiPeuBSdrTl3DAYDl156KS1atCA7O5s1a9bg09uoesAbn4qptIA1a9agqmrYN1E6HRkZGTRs2JCioiL2lpqEYIQJxS6JIpe2zXzRRRfpbI1GrQRDluWxwAwgBjjxk6USUU7gufHrr78CVd5FBGR5V4ctSpKEqqo8/PDDrFq/UW+zzhlPgzSseRsoKCggJycnIhP4qqmunlxUVERBhfiohgs1z1WoXJ+1jZJ6HngHyARST/hqFhjTwg+Px+NfS/U0aK6zNfVDpIYt+mKT8Zm06JNffvlFZ2sCT2KiFt5d4YlcTyrSKK86V2azmbi40Ii2rO0jcAIwTVGUyIpZq2eysrIoL9eiUDwNQ+OJ4FypDlus9jAiJmxRMuBt0BzD4Z389NNP/l4SkUp1BrRPlDwPG3yqJhh6ZXWfjNoKxixgHGHWXzvY/PDDDwB4YxqhWkPjieBcOVnYIobIKJvtSWyB+fBONm3axJEjR2jUqJHeJgWM4mKtkZfdLBQjXIg1abuFDocDh8OBzWbT2aLaC8Y/gfWyLP8F2AvH73sqihLZYSa1wOfz8eOPPwLgSYyMZRs4RdhihJTr8SQ0RzUYweflp59+4pprrtHbpICRl5cHQLItEkIWooOkGucqLy+PzMxMHa3RqO1HfxZQBiwC1gDrTviKerZu3UphYSEAnsQMfY0R1A6jyb/X9P333+tsTOAoLCykoKAAgJZxokFGuJAS48Ne5WVs2bJFZ2s0authXAp0VRQl/MNjAsTKlSsB8Noa+GsVCUIfT2IG5qP7WL9+PSUlJSQkJOhtUr1THeptklQyE4RghAsGCdo08JB12MKaNWtCwgOurYehAOIueApUVfULhiexpSgHEkZ4GqajSga8Xi8///yz3uYEhGXLlgHQMcmNJXT2TwW1oHOyVu/1l19+obS0VGdrau9hPA+8J8vyNGAXcFyjakVRvqpvw8KJHTt2+HsriOWoMMNowZuQhqk4hx9++IEBAwbobVG9sm/fPjZs0PJquzd16myNoK5c1tjFhztU3G43ixcvZtSoUbraU1vB+Kjq35dO8ruoT9yr3uz2WeLw2YPfNlFwbrgTW2IqzmHNmjVUVlYSExOjt0n1xkcffYSqqiRZvf6nVUH4EGtW6d7UyXd5NubOncuwYcOwWPSLUqxtLakIiYsJDMeio1qI5agwxNswHRUJl8vF2rVr6dGjh94m1Qt79uxh6dKlAAxs4cAkPsVhyeAWlazMs1JYWMiCBQt0zRmq0yUky3J7WZavlWU5VpblVrIs1+nuKMuySZbl/8iyfEiW5aOyLL8qy/IZ5VKW5TtkWQ7JAPIDBw74G/FESrJetKGaY/yVhSMl61tVVaZPn47P5yPZ5uWqZg69TRKcJU3sPno105YTP/jgA44ePaqbLbXt6Z0gy/JXwGZgHtAE+BeQJctyXWpgTAUGAkOAYVX/Tj3D3OloeSAhyapVqwBQDWa8cfo2NxGcPd6G2mW8atUqf0OecOabb77xR0eNPq9CbHaHOSMyK7AZfZSXl/Paa6/pZkdtPYyX0arUNgcqqsbuAYqAf9fmDWRZtgF3AX9TFOUXRVFWAn8FJsqyfLpF47fROv6FJNUfSk9CMzCIT2W44mmQDsCRI0fCvnXrkSNH/DeVC5NcXJoi9i7CnQYWlVGttVvvt99+668qEWxqKxiDgQcVRcmrHlAUJRvthl/bLO9OQCxQM0NqZdVYp5O9QJbl24BGwCu1nCOoeDwefwSKt0GaztYIzgVfTCI+k/bcUv0QEI6oqsqLL75ISUkJNqOPW+Rysa0WIfRJcyI30AJUX3rpJQ4fPhx0G2obJRUHVJ5k3EjtRScNKFcUpbh6QFGUElmWK9A8l+OoWop6Hk2QzrrWhsMRuLVbRVGOFRtMEM2SwhpJwpuQiuHIbtavX8+wYcP0tuis+OKLL/zLpDe2rTiuvIQgvDFIcHv7Mp5Y3YDi4mKef/55pkyZ4i8sGQxqKxhfA09X9cUAUGVZTkELs/2mlu9hB04WCO7kj02ZAGaiVcjdJMvyWQtGIFPqV6xYAYDPbEe1Rl6GcLThTUjFfGQ3GzduZNOmTUH9INYH+/fv58033wTgssZOrhR5FxFH4xgfN7Ut562t8axdu5bp06fTu3fvoM1fW8H4K7AAOIx24/8GrQ/G78DY07yuJpWcXBisHNsXAUCW5fFovTZOuyFeGzp06HCub3FK5s+fD4A3vokIp40AqoMWHA4HcXFxtGoVPp2JKysreeWVV/B6vSTbvGIpKoLp3tTF5iNOfj5gZfHixfTt2xdZluvt/U/3kF2XxL1BaHsNHapetxXYgOZ9dKnFe+QCsbIsxyuKUgpa9BWaAO0/4dixQDvgaNV/hLHq+DJggqIos2tpd0BLAiuKAoA3tnHA5hAED5+tAarRguR1sXPnTs4//3y9Tao1//73v9m/fz8GSeWuDmXEijLmEYskwc1yGbtKTByohP/7v//jrbfeCkqTpVP63LIsXyXL8l2yLN8F9ALuANqjZXa7gfOAJ4HWtZwrCygHamZF9aoayzrh2LFowtSp6uueqvFOwBe1nC+gFBYW+jedIqF3twBtHyNWO5fbt2/X2Zjas2LFCr7++msARmZWcF4DUWAw0okxwV0dSjFKKnl5ebz66qtBmfd0HsZhYDJaD28JuBuo2T1eRSt5/rfaTKQoSqUsyzOBabIs31z1nq8BryuK4pBluQFgURTlkKIox3kcsiy3q3qPnbX7swJP9Q1FRcIXE7mNd6INrz0JU8n+sBGMw4cP869//QuA9okuBrUQCXrRQqsEL6NaV/DRzliWLl1Kjx49Al6l4JSCoSjKJrQe3siyvAIYrijKuaYYPgLEAAsBDzCbY138/gNcBWSc4xxBYe/evQD4bAlgrO3KniDU8dm13tf79u3D5/OF/Mb3tGnTKCkpIcbo47bzyzGIfYuoon+6g/WFFpQiM//617/o0qULdrs9YPPVtpZUvWzDK4riBCZWfZ34u3Gned2XaB5JyFDd29pnE1XfI4nqXiYOh4ODBw/StGlTnS06NVlZWf5IvevPqxDd9KIQgwS3tSvjsdUNOXLkCLNmzWLChAmBmy9g7xzh5OfnA1UehiBi8NUIj64+x6HKO++8A2hd9K5qJkJoo5Umdh8D0rU0uXnz5gW01pQQjLOkuv+Fag18ZIIgiBjN+ExaZF11W9NQZMeOHWzcqDXAHJ5ZIZaiopyrWzqwGX243W6+/PLLgM0jBOMsKS7WEtZVU+T0ThBoqGZNMKrPcSjyzTdavmzjGC8XJbnPcLQg0okxqfRI1bzM6g6LgUAIxlngdrv9JUdUk37NTASBQTVq+aWh0BLzVGzatAmAi5NdUetdmA0nzzU51XikU90gKzs7m6KiooDMIQTjLPB6a0QXS+K/MOKoOqc+X+huIlcHXWTER2/ORduGJ//b5VOMRzqtEo7dl7KzswMyh7jbnQVSzZoLEdA7QXAi4XNOjVHqXQAMSK/Eajz+XNmMPvqnn6xOauRjlAJ/3QrBOAssFou/r67kEdEpkUb1OU1ICN0IuOoyEIWO6P0IZyZ4mXD+sWXDi5OcPHJxCZkJ3tO8KnI5VHmsH0+gyoRE79V2DkiSRHJysva9q1xnawLIqRpCRXKjKFXFUHVOk5KSdDbm1HTu3BmADYXRvYeWHndMHG5oUxG1YgGwodAMQGJiIhkZGQGZQwjGWdKypVZx3VgR/CYmweJULWcjuRWt5CxF8mqbh4H60NUH1SUgdhSbyaq6UQiilzK3xOIcLWKze/fuAatQIATjLKmuZGosK4jYfQxXkw6ohuOLAagGM64mgSsZrzfGMi2/xmKxkJmZqbM1p+aKK66gY8eOAHywPZYSVxRvZkQ5PlW7BsrcBmw2GzfddFPA5hKCcZZceumlABicZRgi1MvwxaVQ2aqn/2d3g3Qq5AH4Irg6r/nIXgAuvvhiTKbQrREmSRL33nsvJpOJQw4jL2clUBmdwUFRz/922Vl1QAsFHzduHI0bB67dghCMs6Rdu3Y0a9YMAHNheFQ2PRt89mOVeJ0tuka0WEiucowluQD86U9/0tmaM3Peeefx+OOPI0kSe0pN/N9vCRx1Ck8jWvD4YNZ2O4uztaWoAQMGcP311wd0TiEYZ4kkSQwePBgA86EdSK6KM7xCEOpY8jchqSoJCQlceeWVeptTK3r37s19990HwO4SM0+taciu4tD1jAT1Q6lb4qWsBJblHtu3mDx58vEh/wFACMY5MGzYMOLi4pBUL5b96/U2R3AOSI4SzIe0DoojR44MaIno+mbo0KFMmTIFm81GkcvAP9YnMH93DO7QzTsUnANrD1l47NeG/H5UC3a47rrreOaZZ4KyhCoE4xyIjY1l7FitpbmlcDvG0tAtVic4DaqKbe9PSKqXpKQkhg8frrdFdaZnz55Mnz6dZs2a4VUlPt9r58k1DdghvI2IocgpMW1zHK9uiqfYZcBqtfLII48wadKkoO23CcE4R0aOHEnr1lqXWtvu70Ek8oUd5gO/YyrVSpnfc889QemNHAgyMzN55513uO6665Akif3lJp5d14DXN8dxqFJ81MMVpxc+2xPDg6sSWX1Q29zu1KkT//3vfxkwYEBQbRFX0TliMpl45JFHMJvNGFxlxOxeGbFhtpGIsbQAa+5qQNsP6Nmz5xleEdrExMQwadIkpk2bRqtWrQD49aCVh1c15OOddkpF+G3Y4PHByjwrD/2SyKd77Di9EnFxcdx///288sorpKWlBd0mIRj1QJs2bbj33nsBMBXnYs3+VYhGGCBVFmPb+S2SqpKens6DDz4Y8E3DYNGhQwdmzpzJ5MmTSUxMxKNKfJUdwwO/JPK/nXaRtxHCVAvFw6sa8s62OI66DBiNRkaMGMHs2bMZOnSobq2DxQJnPTF48GAURWHhwoVYDv6OarbhatZJb7MEp0BylWPf/jUGj4O4uDieffbZsNrorg0mk4mrr76aPn368NFHHzFv3jwqKytZlB3DN/tt9Elz0D/dQSOr2B0PBVxe+LHAyqJ9MRxyHCu/06tXL26//XaaN2+uo3UaQjDqiepEqqKiIn744Qes+9eDJOFKvUhv0wQnIDnLsCuLMbjKsVqtPP/88yFdBuRcsdvtjB8/npEjR/LJJ5/w6aefUlFRweLsGJbm2OjW1MmgFg7SYqO3DpOelLklvt1vY2mOjRL3Mc/hqquu4qabbgqpigNCMOoRk8nEE088wWOPPca6deuw5q4DnxdXs4shQpY6wh3JUYJd+RqDqwyz2cwzzzzjL7ER6TRo0IDbbruNUaNGMW/ePBYsWEBpaSk/5Nv4Id9GpyQXA9IrOT/RIy7XIHCgwsCyXBsr8204vdp/uMFgoHfv3vzlL38JKaGoRghGPWO1Wpk6dSpPPfUUq1atwpr3G5K7EmfLK0SzJZ0xlB0iZscyDB4HFouFf/zjH1x22WV6mxV0EhISuPXWWxk9ejSLFy9m7ty5HDhwgN8OW/jtsIX0OA/9mzu4vIkTSwQXJtYDVQWlyMTXOTFsKDSjogmFzWZj8ODBjBw5ktTUVJ2tPDVCMAKA1WplypQpPPfcc6xcuRLLIQWDq5zK1r3BKCqL6oHxaDYxu1cg+bzExsbyj3/8g4svvlhvs3TFbrczYsQIhg4dysqVK5k7dy6KopBTZuLtbXHM3WWnT5qDPmkOGlpFEMe54PbBqgNWlubY2Fd27LablJTEsGHDGDJkCA0aNNDRwtohBCNAWCwWnnrqKWbMmMHcuXMxFedi/30hlW3+jGoL3cY8EYeqYsnPwrJ/PRKQkpLC//3f/4Wku68XJpOJP/3pT/Tp04fNmzfzySef8OOPP1Lihs/22lm4L4bLm7jo17zyuDaggjNT5NT2J77df/z+RNu2bRk5ciS9e/fGbA6fh0ghGAHEYDBw11130bRpU6ZNmwaOImJ//4LKzF54G6brbV7k43Vj2/MD5qN7Ae1D+txzz5GSErkFFM8FSZLo2LEjHTt2JD8/nwULFrBo0SLKy8v5qcDKTwVW2jZw0z/dQZcUFwaxz3FK9pYaWZJjY9UBK1712P7ElVdeyYgRI7jwwgvDMoRbCEYQGD58OBkZGTz99NOUlJQQs2MZrtSLcKVdLPY1AoSh4ggxu77F4CgBoG/fvkyePBmr1aqzZeFBamoqd911F+PGjWPJkiV8+umn5OTksL3YzPZiM8k2L/2aO+jVzEmMSSxXgdaXYkOhmSU5MWwrOuY1xMXFMXjwYIYNGxbS+xO1IaiCIcuyCXgZuKFq7lnAZEVRXCc5NhF4EbgasAArgHsVRdkfPIvrj86dO/Pmm2/y1FNPsX37dqz5WRjLDuDIvArVElnx/7qiqpgLt2PdtwpJ9WI0Gpk4cSIjR44Myyc6vbHb7Vx77bUMHTqUNWvWMG/ePNasWUOhw8icnbF8uieGPmlO+kVxPkd1/sTi7BgO1Oir3bx5c0aOHEm/fv0iJscn2B7GVGAgMARNBN4HXMDkkxz7LpAGDAUqgZeAL2RZvkxRlLBcSE1NTeW1117jjTfe4LPPPsNUWkDs5gU4Wl2JJ7Gl3uaFPx4ntr0/+ZegGjduzFNPPUWHDpHbITBYGAwGunbtSteuXdmzZw/z5s1j6dKlONxuvsqOYUmOjW5NnAxs4aB5XFh+POtMmVtiea6Npbk2SmvsT3Tu3JmRI0dy+eWX65aRHSgkNUglLGRZtgGFwBhFURZWjV0DfASkKIpSWePYZOAQcLmiKL9WjaUBuUAnRVGyajPnunXr1C5dutTvH1JPrFixgpdeeony8nIAXCkyzvTLQi6KSnKUELdpHgBlHUeG7Ia9sSQf2+6VGNxaX5IrrriCRx55JCwiT8KVo0eP8tlnn7FgwQJKSkr845ekOBmaUUnL+MALx4EKAw+uSgTgn5cfpYk98F5OiUticXYMy/fbcNTIn+jTpw/XX389bdq0CbgNgWTdunV06dLlpO54MD2MTkAs8H2NsZVVY52AX2qMVwKDgN9qjFVfCTEBtDFo9O7dm3bt2jF16lQ2bdqE5ZCCqSQPR6ueeOOb6G1e+OD1YM1di+Xg74AWnXbXXXcxdOhQsQQVYBITE7nllluOy+coKChg7SEraw9ZuTjZxdCMCjIjJLKq2CWxaF8M3+634fIdnz9x3XXX0bRpU50tDDzBFIw0oFxRlOLqAUVRSmRZrgCOK5KiKEo5sPiE1z8AlAAb6zKpw+E4O2uDQGJiIi+88AL/+9//mD17NjhLidm2CHfTjjjTLgaDiEk4HYayg9j2/IDRoV1SrVu35sEHHyQjIwOnU5SZDxaSJDFo0CD69evHihUr+Pjjj9m/fz8bCi1sKLRwaYqTkZkVpMaG5x5HpUdiUbaNJTkx/oxsu93OkCFDuPbaa/1ebCjfa+qLYN6R7MDJPsVO4LShK7Isj0YTjLsVRalTL9QtW7bU5XBduOiii0hKSuKjjz6ioKAAS8EmjEXZOFr1wBcXuIbuYYvPgzV3PeYDW5BQ/csBffv2pby8PCzOeaTSrFkz7rvvPn777TeWLVvGwYMHWXPIyrpCCz1TnQxvVRE2SYAeH3yz38YXe2Moq9qjsNls9OzZkx49emC328nNzSU3N1dnS4NHMAWjkpMLgxU4pQjIsjwOeBv4t6Iob9R10nDZ8OzQoQO9e/dm1qxZzJ8/HxzF2Ld+ibtJB5xpXcAovA3Q+lfY9v7oD5dNT09n8uTJyLKss2WCmnTs2JEbbriB5cuXM2vWLA4dOsR3eTZWHbBwbatK+jZ3YArh/eBNh818uCOW/Aot6slisTB06FBGjRpFfHy8ztYFltM9cAXzLpQLxMqyHK8oSimALMsJaJ7HSUNlZVm+H3gFeEFRlEfPZlKbzXaW5gYfm83GpEmT6N27Ny+++CJ79+7FcmALpqJsHBnd8SY009tE/fC6qvYqtgHaJuPo0aO5+eabRW5FCDNkyBD69+/P559/zgcffEBZWRkf7YxlZZ6VcXI57RI9ept4HEccBmbtsLPukHZNSZLEwIEDGTduHI0bC28/mBqfBZQDPWqM9aoa+0PUkyzLt6KJxRNnKxbhSvv27XnrrbcYO3YsRqMRg7MUu/I11j0/RmULWGNRDrGbF/jFIjMzk+nTp3PHHXcIsQgDrFYro0aN4sMPP2Tw4MFIkkRehYmpGxrwwXY7zhDYE1dV+D7PymOrG/jFon379syYMYOHHnpIiEUVQQurBZBl+V9oeRU3AxLwAfA/RVEelmW5AWBRFOWQLMtNgV3A52h7FzU5qihKre6aoRxWW1t27tzJiy++yPbt2wHwmWJwtrwcT2JGUEqm6xlWK7krsWavwnxkDwBms5kbb7yRMWPGhFX9HcHxbNu2jZdeeomdO3cC0NjmZUKHMto0qLu3UR9htSUuiZlb48g6bAEgPj6eu+66i/79+0dcHkVtOF1YbbD/Nx4BlgILgU/RBOHxqt/9B1hT9f3VaEtVY4D8E776BtFe3TnvvPOYPn06EydOxGq1YvBUErNrBbady5Fc5XqbFxhUFVPhDmI3feoXiwsuuIC3336bm266SYhFmNOuXTtmzJjBuHHjMBqNHHQYmbo+gcXZtqB3NlaKTDyxuqFfLLp168Z7773HwIEDo1IszkRQPYxgEwkeRk3279/Pyy+/zPr16wFQjWaczS/FnSIHzNsItochOUux7f0JU0keoIUvTpgwgWuuuUZ8gCOQHTt2MGXKFHJycgAt6W9C+zKstezDcbYehqrCklwbH++041MlbDYb9913H/3794/6/J1Q8jAE50BaWhovv/wyDz/8MPHx8UheN7Z9PxOz7SskR/GZ3yCUUX2YCzYTu3mBXyyqn/b0bHovCCxt2rThzTffpHfv3gCsPWTl+fUNKHYF7qbtU2H2DjtzdsTiUyVatGjBjBkzGDBgQNSLxZkQn8Iwozpq4/333/d/yExlB4jd/Bnm/E2ghl9ylKGyCPvWr7DlrEbyeUhMTOSpp57iueeeE5uNUYDdbufJJ59k4sSJAOwuNTFlbQMOVdb/7cnjg9c3x7E0VysY0a1bN2bMmBHRPd3rEyEYYUqjRo38N9Xk5GQk1Ystdw32rV9iqCzS27zaofqw5G/EvuVzjOUHAejfvz/vvfcevXv3Fk97UYQkSYwePZqnn34as9nMIYeR5zckUFiPouHxwetb4lhTFQU1bNgwnn322YipJBsMhGCEOd27d+fdd99l0KBBABjLC7Fv+RxzwRaCvoNYByRHCfZtX2HNXYukev2d8B599FFRMDCKueqqq3jhhRewWCwUVolGkfPcHxx8Krz1e5w/ZHbs2LHce++9GI2iaXldEIIRAcTHx/PQQw/x4osvHvM2cn4lRlmM5CzT27zjUVXMB7cRuwBmHN8AAAzvSURBVOUzjGWaVzFo0CDeffddunbtqrNxglCgS5cuTJ06FYvFwiGHkX9vij/nXI35u2NYdVATizFjxjB+/HjhwZ4FQjAiiMsuu4x3332Xvn21yGNTaQGxWz7DdGSvvoZV43Fi27kc276fkXweGjVqxPPPP89DDz1EXFyc3tYJQohLLrmExx57DIDdJWbe2Rp31g7zzwUWFu7Tlp0GDhzIHXfcIcTiLBGCEWHEx8fz+OOP88wzz1RFUrmI2fUt1r0/g0+/MgzG0gJtY74oG4AePXrw3//+lyuuuEI3mwShzVVXXcXtt98OwKqDVlbk1T2rP6/cwLuK9jDSqVMnHnjgASEW54AQjAilV69evP3221x44YUAWA5tw77tq+AvUakq5gO/E6MsxuAux2Kx8MADDzBlyhQaNmwYXFsEYccNN9xA9+7dAZi9I5b88trfsjw+eGNLPE6vRKNGjXjyySdF0uc5IgQjgmnSpAmvvPIKN954I1C1If77FxhL8oNjgM+Dbc/32LJXIakq6enpzJgxgyFDhoinPEGtkCSJhx9+mOTkZNw+iXe2xeGr5dLU4mwb+8q0+qqPPfYYjRo1CqCl0YEQjAjHZDIxfvx4pk6dSmxsLAaPg5jtSzAd3hXYiT0OYpQlmKvm6d69OzNmzCAzMzOw8woijoSEBO6//34Atheb+SH/zEtThZUGPtur7VtcffXVXHLJJQG1MVoQghElVCcoNW/eHEn1EbN7JZa8WrVGrzOSsxT71kWYyg4AMG7cOJ599lliY2MDMp8g8unevTs9e/YEYP5uO44zbMd9stuO2yfRsGFDJkyYEAQLowMhGFFEeno606ZN44ILLgDAun8dlv3r6zVfozq/wugoxmQy8dhjjzFu3DhR2kNwzkyYMAGTyUSRy8CyXK3PTZLNR7LNS7LNS5JNq3KQXWbklwOaF3LLLbdEfMOjYCI+xVFGw4YNefnll+nWrRsA1rzfsOxfVy/vXS0WBlc5NpuNF154gX79+tXLewsEaWlpXHPNNQB8nROD0wsmA7x4eREvXl7k7+C3cK9W9qNZs2YMHjxYL3MjEiEYUYjVauWZZ56hRw+tl5U1fyPmgv9v715jq67vOI6/z7WnFto1aVekTBpL8zOQAkJCatQJQQ0YYlicjtkEZ8yicYMn+IBApolZGp/4YF72xGVZ4mUz48nitsQsM8EQWRCCxHXzOzdQLIpc7M3eoPTswf/09LQc2n+B8/+Vns8raXrO7/xavml6+uH3//8u/7ym7xm7OMRN/3mX+MXBfFjourFcb9u2bSORSNB/Mc6B3L2MZJx8WJwbinPoTLBVeXt7O8mkjja+nhQYZSqVSvHcc8/l10FkvjhE8pvjV/fNLo1S+enfiI/0k0qleOGFF1i9evV1rFYk0NDQkN908++nLj8/470vM2QJ7l2ML2CV60eBUcaSySTPPvsst912GwCZEweID3XP7ptks2Q+/4DEwDkA9u7dq7CQktq6dSsAXQNJjvdPjCDGsuRnUD3wwAOk02kv9c1nCowyV1lZSUdHR7AH1dgomf++B5cuhv765LlPSZ0Pjtp84oknWL9+fYkqFQmsWLGCJUuWAHDw9EQo/Ls7Se+F4E/apk2bvNQ23ykwJL8KNh6PkxjupSLkTfDYyLdkTv4DgLa2Ntrb20tZpggQLObbuHEjAEfOpvOXpQ7ndqJtaWnhlltu8VXevKbAEABWrlyZXxGe+vpfxHM7yV5R7lJUbGyU6upqdu/eramzEpnx7ULOjyT4YiBBNgsfnQu2/bjrrrt8ljav6R0uee3t7SxdupQYBCOHadZnJHq7SPZ2AbBjxw7tCyWRamlpyW/10flNitNDcc6PBGdbaJv80lFgSF46nWbnzp1AsO9UsvtzsukqxtILGEsvIJvOrdTOZqnoOgwEI5N7773XV8lSpmKxGKtWrQLgk54Un3QHo4uqqipaWlp8ljavKTBkkrVr1+bXT6S/OgaxOAOtDzHQ+hDEg//BJXtOksjNpnryySe1kaB40draCsDxviTH+4LZUsuXL9cpeiWkwJDLbN++HYDE4HkS/aeDoIhPvAlTX3cCsGbNGlasWOGlRhHnHAC9F+J8dD49qU1KQ4Ehl2ltbc2/8VJnP5n0Wnyoh2T/aQAefvjhyGsTGdfc3Jx/PD6ddtmyZb7KKQsKDLlMLBbL78GT7D4JoxfyryVzay7q6upYt26dl/pEADKZDA0NDZPaNJ22tBQYUtSGDRtIpVLEspdI5o5VJZsl9c0JAO677z5dKxbvGhsbJz1fvHixp0rKgwJDilq4cGF+i49k7xcAxId7iY/0A5rrLnPDokWL8o9ra2vJZDIeq5n/It3K0TmXBF4EHs39268Dz5jZhSv03wv8DFgI/An4uZn1RFRu2Wtra+PDDz8k2fslZLMk+k4BUFNTk99/SsSnurq6oo+lNKIeYXQAm4EHga25zx3FOjrnngZ2Ao8DG4DlwG+iKVMAbr/9dgBil0aID/eQ6A9O0Fu1apUuR8mcUHhOd21trcdKykNkgeGcywBPA7vM7KCZ7Qd2AE855yqLfMku4Jdm9q6ZHQYeAx5yzumuVkSamppYsGABAIlvz5LIbRcyPv9dxLfC0/Rqamo8VlIeohxhrAaqgPcL2vbn2ibth+2cWwTcWtjXzD4GuoE7S16pABCPx/OrZhO9p4hfHAQ0113mjsLA0JnxpRflPYxGYMDMescbzKzPOTcILCnSF+DUlPavivSd1vDw8GzrlAJNTU0cPXqUVPeJfFtjY6N+rjInFJ6oNzY2pt/LEosyMG4CRoq0jwAVRfqOvzZT32l1dnbOprtMMfVeRXV1NZ999pmfYkSm6O6eOPArFovp/V5iUQbGEMX/2FcAg0X6jr/WP0PfaWnrimszOjrKvn378s+XLl2qn6nMKXv27OHMmTNs2bJF02qvg+lCN8rA6AKqnHMLzawfwDlXTTCamHrpqSv3+WbgXEH7zUX6Tku/QNdm6srZxYsX62cqc8r999/vu4SyEeVN72PAAHB3Qds9ubZjhR3N7DRwvLCvc64V+A5wsOSVSt7Uue319fWeKhER3yIbYZjZkHPuNeAV59xjQAx4GXjVzIadczVA2szO5r7kJeB559znwGngNeCPZnYyqpoFUqkU1dXV9PX1AZPnvYtIeYl0pTewG6gE3gFGgTeBvbnXfgWsB5pyz18C6oDfASngzwTrOCRitbW1+cDQ4iiR8hVpYJjZCPBU7mPqaz+Z8jwL/CL3IR5VVk6sqyyc9y4i5UWbD8qMCk/UG1/5LSLlR4Ehs6LVtCLlS4Ehs1J4eUpEyosCQ2alomJWC+1FZB5RYMiM7rjjDiCYYqsRhkj5inpardyAHnnkEerr62lubp602ZuIlBe9+2VGmUyGzZs3+y5DRDzTJSkREQlFgSEiIqEoMEREJBQFhoiIhKLAEBGRUBQYIiISigJDRERCmffrMI4cOeK7BBGReSGWzWZ91yAiIjcAXZISEZFQFBgiIhKKAkNEREJRYIiISCgKDBERCUWBISIioSgwREQklHm/cE+ujXMuCbwIPErw+/I68IyZXfBamEiOcy4G/BX4i5m94rue+UwjDJlJB7AZeBDYmvvc4bUikRznXBx4Gdjku5ZyoMCQK3LOZYCngV1mdtDM9gM7gKecc5V+q5Ny55y7FdgPbAF6PJdTFhQYMp3VQBXwfkHb/lzbai8ViUxoAz4G1gC9nmspC7qHIdNpBAbMLP9mNLM+59wgsMRfWSJgZm8BbwE45zxXUx40wpDp3ASMFGkfASoirkVEPFNgyHSGKB4MFcBgxLWIiGcKDJlOF1DlnFs43uCcqyYYeZzyVpWIeKHAkOkcAwaAuwva7sm1HfNSkYh4o5veckVmNuScew14xTn3GBAjmPP+qpkN+61ORKKmwJCZ7AYqgXeAUeBNYK/XikTECx3RKiIioegehoiIhKLAEBGRUBQYIiISigJDRERCUWCIiEgoCgwREQlFgSEiIqEoMEREJBQFhoiIhKKtQURKyDn3a6DVzO4uaNsOPE+wN9fbwLbc45Vm1u2lUJEQNMIQKa03gDudc98raHuU4KS4LPBT4IfADxQWMtcpMERKyMw+AE4APwJwztUDGwk2cQR428wOmdlhTyWKhKbAECm9NwguO0EQHJ1m1pl7/j8/JYnMngJDpPTeANY655qBHzMxuoDgGFyRG4ICQ6TEzOxT4BDwOLAO+L3fikSujgJDJBqvA7uAA2bW5bsYkauhwBCJxh+ANJMvR4ncULQOQyQaDcAFYN94g5k1eatG5CooMERKyDn3XeD7wA7gLTPr8VySyFXTJSmR0loA/BaoBPZ4rkXkmsSy2azvGkRE5AagEYaIiISiwBARkVAUGCIiEooCQ0REQlFgiIhIKAoMEREJ5f9GTptXDmNLTAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#年份和温度特征的相关性\n",
    "sn.violinplot(data=train[['yr',\n",
    "                        'temp']],\n",
    "              x=\"yr\",y=\"temp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb126e48>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#get the names of all the columns\n",
    "cols = train.columns \n",
    "\n",
    "# Calculates pearson co-efficient for all combinations，通常认为相关系数大于0.5的为强相关\n",
    "data_corr = train.corr()\n",
    "sn.heatmap(data_corr,annot=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cT4PBQGhoQllYWASurgYePHhEXFwcJUsWY8qUEUyaNAuAgIBAfv/93ktneBHOLtkID4tIyGk0YmeX8NJz/OAJQh+FJtnmwV8PWD7vR/q2HMiyuasYM2d4uuSKSJIr/rm57v/1kBXzfqRfyy9YPncVo+d8abM8men8fBZngzNhoeEJOROdGwABB44RkqzdAHZs3IMpHV8L9C7ZiEx0jTXGG9FajqcxLp4Iy93U5sM78PvFm/x94x4RweGUrurGiN1fUfvTxgT87Jdi3WmlccqG6XHCcw1jPGjN2TSG7NiXcSfq50VEzBiGrl4LtPnfQmPIjjb/mzz+agTRW1bj1H1I+mTLljSbyWgErfl4alyz4+DmzuP/LSR07BD0TVqiLfiW5Xcw4jrxG/QftSAmMB2mIL1OJmP6/mQymXqqTwr8VVWdD6AoypdAb8ANCARyAfdUVb0J3FQUpS7wt6qqkYqihANxT6YOKYpyGFioqupJS70zFEUZDbwDnLAsC1JV9cl934mKogwAyquqeiJ5fVlBbFgk9paREQCNRovJ0rl+u2VVnPLnpOba4TgXyosxJo6IO/9wz//sa8nmrHckIiqhE2k0mrC3XPAPX7zF/ZAIto4xz1Hs/e1G3IsVoGyR/ATde4jBSZfk8wDpJT48Em3i9tNqwNJ+xsgY/vp+C8Yo81zY0MPnyPZOUSIv2XZ6VGhoOAaDS0IGjZb4+PhEZQnTtFxcnAkOfvrFG+DBg0cEBd3k8mXzLftdu/wpV64sfn6H05zxwcNHXAu6yaVL5rp37vLHw6MsRYoUokoVTzwrlgPMo9VPPiMAsHLVr4SGhWFwMf9+BoMLwSHmEcfZc763doK3bd+Lu/t7bN2255k5kreVVpu4rRL2A+BicCYkOISwMHMbRkVFmfcfHEqHDp/wZsH87N61hiJFChETG8vNW3esI7t167VCUYqzccNySpd5uc7Zk/2llDEsLAyXRBkNBmdCQszHs1q1D5k9eyK+vp/bdCQ4NRHhj3F2yZYs57NfDC+dUa2/y9nj58mbP0+65Mrm4vRSuS6nY65/w/kJEBEWkeR4ahI97zJSVPhjdM5Jr7HGRMfTXudAh+k9iYqI4ueR5s9lNOj/CXsWbubwj3soWLow3RYMZEoD23ewTZGP0egTnmtotdZvkjGFhRJ/Q8UUYh41j1PPYVe4OKbwUGJPmTvU8ZfPos3/ls1zAZgeP0bjlHA80WisU19MYaHEXVUxBZs/fxR74Qz2xUoSc9f8mY7QkQPQvlUY19FTCe7RLl3yCdv7t434X3nyH1VVn1z1HFRVfQhMARYpivKHoiiLAYOqqqn1nFYCbymK8o2iKFsVRfkDcAHsEq2TfCJiGOBgk9/iX+if41coWNMdgNweJQi+nHCj4/TEn9j90Rj2fTKJG2sOoC7a/to6/QDuxQpw6OJNAM7e+JOSBXNby1yz6dA52uNob4fOwR6Dk46wx+YO9m9X7lAlnaf4PBF+/BI5appvKDl7lOKx5YPFAPpiBSmzYTJotWjs7TB4liHinO07ZAEBgdSv7w2Ap2c5Lly4bC0LDDyNl5cnOp0OV1cDpUuX4MIFNcV6bty4jYtLNooVM7edl5cnFy9eSXHdl3X9+m1cnJ0pXrwoAFWqmOtW1Wv8/PNGatVpSaPGHfn11y0cDzxNrTotqVWnJUv/t5ojRwJp0KAmAPXre3Po0DFcXQ2cObUPZ2fzC5u3txcnTz7/uXkk4DgN6pvrquTpwfnzl6xlx4+fpkqVxG1VkvMXVI4cOU6D+uZpWvXqeXPo8G98+eUkvKo0pnadlixfsYbZsxaxa5c/Q4b0pX37jwGIiIgk/hW+Ti4gIJB69RKO5/nzCccrMPAMXl4VrRkVpQQXLlyhWrUPmTlzDE2bduLkyRf/zENanD1+ng9rVgLgXY8yBF16/nPbd2BnWnf7BIAS7xTnr7t/P2eLl3fuFXL5DOxEq24fW3IVs2muf8P5CXDq2Fmq1TJ/Jsqt/HtcvXTNZnWnxfVAlXe9zQMDRcuV5K56O0n5p4sH8/ulW6wevhiT0Xzn4XFIOFGWuwRhD0LQG5xID3FXzmPvbn6u2RUvQ/ydG9ay+JtXsHurKBoXV9BqsStRhvi7t4hXz+Fg2UZbuBjGB7Y/BwDiLp3DoYJ5P/bKO8TfSsgWd03FrsjbaFyzg9bOXH77JvpP2uPoXde8UlTkv//rMI3G9P3JZP5tI/4xKSzTAKiqOlxRlP8BTYGGwG5FUUapqjo5hW2WALWB5ZafXpin7rzQvrKi37cHkr9aWWpvGoMGDUcHLkTp0YDwm3/xx66Tz68gHdV0K85R9Q6dvl4LwLj2tVix7xSF8manRtliHFXv0PHrtWg1GtyLFeDD0uZ5uTf/esQHpW0/dzglj7b/hms1d8psnAIaDTcGzCVfjyZE37hH8O7jPFh3gHc2T8UUF8/9X/yJumL7GWQbN+6gVq2q+PmtQ6PR0KPHID77rBtBQbfYunU33367lL17f0Gr1TJmzIxUvwUkNjaWnj2HsGzZXDQaDUePnnilqSqJtWnTDBdnZ77/YRU9Ph3EyuXzQKMhICCQbdv34ujoyMLvZrBvzy8YXA18992yp6YNfLdwGUuXzGa/33piYmLp0KkPoaFhjBw9lT271xITHcM+v0Nsf4GsGzZsp3atahzYvxGNRkO37gP4vH8PrgXdYMuW3cybtwR/v3VotVpGj55GdHQ0k6fMZskPs/D1bcf9Bw/p2LFPqvX/73+rWfLDLLp2aYOdnR3duw146TbbuHEHNWtWSeF43mTr1j2W47kWjUbL2LEziY6OZsaM0Tg6OrB48VcAXLlynX79bD+NJrH92w/iWa08izbOBY2GSQOm0aZHS36/8QeHdh9JcZsV839kzNwReNX6gPj4eCZ+PjUdch2iYrXyfLdxLhoNTBowndY9PuGPG3dTzbVy/k+MnjucytZc02yWJzOfn4nt2ebPh9U9WbllMRqNhpH9J9D507bcvvk7fjsPPr+CdHJm53FKV3Vj4K/j0Wg0rBy8gJq+jfjn1p9o7LSUqFQGe0d73q1hHsDaNP0ntn61hnbTPqVqh7rYOdjx07BF6ZIt7sQh7N8rj/PoOYCGyMXTcaz/Cca//iDuVABRa37AeYj5uRTz236Mv98k5s8/cOrSH+cx5vMmcumsdMkWE3AQB/cKuE6fDxoN4bOnom/aivh7vxN77AiPly3CdZx5qmL0IT/ib9/AGBqMy+dfoq/TELR2hM+2/fkp0o8mPefcpZWiKCagsaqqWxRF8QcCVVUdlLwcOAt8CXyuqmq0pWwy0ExV1XcURRljqaeCoigGIBioo6rqPsu6+YG7gK+qqkstbyBcVFX9JNG+bgIzVVWdl7i+F/k9Yu9fz7SN/IvbqOevlAGaLX36w5uZyXmfvc9f6TWr+uDE81fKIHGZ+FsTMuu7eXu7zDsu456rWEZHSJU20x5ROPEgc4yOJ1c8e4GMjpAqb6eiGR0hVZOqvvyH8l+HuOD0+ZCyLeTevD/TnaDRQUfTtY+mK/5BpvqdM+8ry8v5B/gEQFGUrwBXoA5w3FIeDuRXFKUY5g/jRgAtFEW5ARQEZmB+/X/R78lMXN9tVVUz71kmhBBCCCEE/745/ilSVTUS+AgoC5zG/I0/54HPLKv8gnmO/kXAHWiHearPRcxTfXYB24DyvJjE9ZWzyS8hhBBCCCFeL5njn3moqqpJ9P8azyn/DaiSSj23MH/NZ2JbnrHfLiksK/qc+oQQQgghhMi0MnXHXwghhBBCiHSTCb9rPz39J6b6CCGEEEIIIZ5NRvyFEEIIIUTWlIm/dS49yIi/EEIIIYQQWYCM+AshhBBCiKxJ5vgLIYQQQggh/mtkxF8IIYQQQmRNmfC79tOTjPgLIYQQQgiRBciIvxBCCCGEyJqy2Bx/6fhncZ+cnZDREVIVd2JbRkdIWchD3vumXEaneMojypG946KMjvGvo9VmzhufJpMpoyOk6iP7ghkdIVV2aJ6/UgbJ80a2jI6QomyazNsV+MP4OKMjCPGfknnP9v+QX9xGZXSEFGXmTj+AofGUjI6QorCVn2Z0BCGEEBls2978GR0hRfGZ+M1vh4wOkJIsNsdfOv5CCCGEECJLMpnkD3gJIYQQQggh/mNkxF8IIYQQQmRNWezDvTLiL4QQQgghRBYgI/5CCCGEECJrymIf7pURfyGEEEIIIbIAGfEXQgghhBBZk8zxF0IIIYQQQvzXyIi/EEIIIYTImozyPf5CCCGEEEKI/xjp+GcmGg0VpvpQZ9NYav4yApei+VJcp/rKIZToWCvJ4rfqV+DD+X1eU9CUnb1wmS59h6Rb/RqNhvnzpnLowCb27l5L8eJFk5T7+rTjaMA2Dh/cTKOGtQHInTsn27f+iP++dfy4agFOTnrr+nny5OLShUPodDoAXF0NbNm0Ar+9v7Jz+2ry5cubprxGo4mJm36j06Kd+P6wm9sPwpKULzt0kbYLttPuu+3su3gHgMiYOD5ftZ+u3++i97J9PIyISlOGxDQaDfPmTeHA/o3s3vV0+/n4tCPgyFYOHthEw4ZJn1/9+vkyaeKXSZY5Oenx91uPohR/5Ty2Op69enYm4MhWAg5vsa77RNOm9VmxfN4rZXySc97cyez338CuXWsoXixpTh+fthw5vJUD+zfSsEGyduvry8QJw6yPW7VqysEDm/D3W8+8uZPRaDRpyjV37mT8/deza9fPFCtW5Klchw9vYf/+DTRIlqtvX18mJMrVrl0Ljh/fyd69v9ClS+tXzpRKUBpM8qHz+rF0WD2CnEWSXtfKd6pD103j6bpxPCVqlgPAwUlHy8UD6bh2FG2WDSFbLoNtM1ly1ZvUlY7rx9Bu9QhyJMvl0ak2nTeNp9PGcRSv6Z6kLFfxAnx+bhF2Ogfb58J8bHtN7sO09TOZ+PMU8hcpkKS8iW9TZmz8ihkbv6L1522TlH1Q70MGzhmULrmeZPOd1JNx66cyavVE8hXJn6S8gW9jJmyYzoQN0/m4v/m5pHPS8cXiLxmzdjLDlo3GkMs1XXL1nNybqetnMOHnyU+1WWPfpkzbOJNpG2fS6vM2Scoq1fuAAenYZpn6tV2jwXNqV+ptGkOdZ2TzXjmYkh1rJllcqH4FvOb3Tr9sr4vJmL4/mYx0/DORt+qXx07nwO4mYzkz+WfKjWn/1DpuQ1vimMM5yTKP8R15f3hrNNpX70Sk1ZJVaxkzdTYx0THpto+mTeuj1+uoUq0Jw0dMYcb00dayfPny0revD9WqN6NBo3ZMnDgMR0dHRo4YwE+rN1CjZgtOnz5Pj+4dAahbpzrbt/1Evnx5rHV07tSK8xcu413rY9as3cSggb3SlNfv0h2i44ws71GP/nXL8fWOk9ay0MgYfjqqsrx7Xb7rXJMZ2wIB+DXwGmUK5mJpt7rUK1uExf7n05QhsaZN6qPX6ahWvSkjRk5h+rRR1rJ8+fLSt48P1Ws0p9FH7Zk44UscHR3R6/X8b+kcevbskqQuDw839u399anO5kvlsdHxzJ07Jz17dqZqtabUqdeaeXOnWOv5+qtxTJr4JVrtq1/qmjSph06vp3qNZowcOYVpydqtT28fang356PGHZgwYZi13ZYunU3Pnp2t6+r1esaNHUzdeq2o4d0c1+yuT71Jedlcer2OGjWaM3Lk1Kdy9e7dFW/vFjRu3JEJE4ZaculYunQWPXt2sq6bO3dOxo4dRN26rahduyVt2jSjSJG3XjlXcko983VtWfOx7Jv2M7VHJlzXnHK6UL5jbZa1GMeqdpNpMKkrAO5tvbl37gYrWk7g4uajePVrZrM8T5SqVx57nQMrmo/Df9pqao1slyRXuY61WdFiHKvbTaGeJReAo4sTNUe2Jz4m1uaZnqhU7wMcdA4MbT6I5VP/h88oX2tZvsL5qN68BkObD2ZIs0GUq1aOIqWLAtBtbA86Du2Upuf781SoVwkHnSNjmg/jp2nL6TAyoW3eKJSPKs2qM7rFMEY3H0rZau4ULl2Emm3rcuNcEONaDufI5kM079fS5rnMbebIsOaDWTF1GV1H+VjL8hXOR7Xm1fmy+RCGNRuMe6I28x3bnQ5DO6fr62dmfm0vZMm2s8k4Tk1eTfm69I2pAAAgAElEQVQx7Z5ax31oS3TJslUY3xH34a0ytN8hXk2m6vgritJDUZRriqJEK4pyWVGUTpblBkVRFimK8lBRlAeKoqxVFKVgou1KKoqySVGUYMu25xVFaZKovLllWZSiKDcURRmcqEynKMo4y/IoRVEOKYpSKVG5v6V8g6IojxVFURVF6ZYev39eT4V7/mcAeHDyGrnc3k5SXqiRJyajiXt+Z5Isvx94lePDlqZHpBdWqGABZk0ema77qFLZk527/AD47dhJynu4WcsqVnTnyJFAYmJiCA0NIyjoJm5ly+DllbDNjp1+1KpZFQCj0Ui9+m14+DDYWsf585dxcXEBzKP/sXFpe2E/dfsfvEqYR53cCuXhwh8PrGVOjvYUyOFMZGwckTHx1pHfDpVL0636uwD8GfKY3C76pyt+RZW9KrJrlz8Ax46dxMPjfWtZxYruHAk4nqT9ypYtg16vY+WqX5g6dU6SunQ6R1q26oaqBr1yHlsdzwcPHuFRvg5xcXHkz/8GwSEh1noCjp6gb9+kdypelldlz0TtdgqPxDkruBOQSrutWvUrU6fNta4bHR1N9RrNiIw038Wxt7MjKjr6lXNVrlwx1VwVKrgTEBCYLFdp9Ho9q1b9yrREud5+uzBnzlzk0aMQTCYTgYFn8fQs98q5kitUUeH6fvM16+6paxRIdF2LfBTO4vpfYoyLxzlvdqJCHwNwfMkODs/bAIBrwdxE3A95uuI0equiwvX9Zy25gsifLNeS+sOfygVQf4oPB6avIS4y/QY53qn4Lqf8zQMFV06plHAraS27f/c+YzuOwWg0YjKZsLO3J9Yy4HL5xCW+G/FtuuUCUCqW4cx+c7Zrp65QzK2EtezBvftM7TQOkzWbHTHRsWxfspn1834BIE/BPISkw/EsU/EdTvmfAMxtVjxZm43vONbaZvbJ2mxhOrdZZn5tf8NT4a6/+Ty4fzKI3MmyFW5UEZPRyF2/s0mW/xN4hWMZ3O+wGaMxfX8ymUzT8VcUxQOYCwwESgFzgP8pilISWAQoQD2gOmACdiqKYq8oigbYDIQBHwDuwDlgqaIojoqi5AN+Br6x1DEYmKQoypP7afMAH6APUA64AOxWFCXxfcKhwG5L+R5ggaIoSe9v2oCDwYnY0EjrY5PRiMbOfIiyK29RpHllzs345antbm86CiaTreO8lDreVbC3T9/PihtcXQgNSZguEx9vxM7ODjB31ENDQ61lYWERZM/uiqvBhRDLNmFh4WTPbp4ysGfvQR4+fJSk/gcPH1GndjXOnvHji4E9WbJ0dZryRkTH4qJPmApgp9UQF59wEcjnmo0Wc7bQZsE22n6oJFpPS/cle1h9VKVKqYLYiqvBQEho4vaLT2g/gyFJ2z5pq+DgEPbsOfBUXQEBgfz++7005bHl8YyPj6d3ry4cPriZdeu2Wrdbu3YTpjSeG+acCVnijQntZnBNyAMQFp56u5lMJv7++z4AvXt1wcXFOcW2fVGuyfZtTJQreVl4uLn9zLkOJqnn2rWbvPNOKd54Iw9OTnq8vb1wds72yrmS07k4ER2WcF0zxidc1wBM8UYqdK5Dlw3juLztWMJyo4n2Pw2nQpe6BCXrENkuV0KHPqVcHp3r0GnDWFRLriqftyBo32n+vnTb5nkSy+biRERYRKJs8Wgt2eLj4gl7ZH4+dhnhw/ULQdy9cReAQ5sPpvtLgZNLNh4na7ek2czPu/YjunDzwg3+tGQzGY2M/Gk89bo04rTfiQzIZW6zziN8uH7hurXNDm8+lOZrxPNk5td2c7aEdkuerWjzypyZ8etT293a9Ju5Jyb+dTLTt/oUAYzALVVVbwHfKopyFTAAbYC3VFX9A0BRlI7AfaA+sA/4HliiqupDS/lMyzb5gDyAA3DHUu8tRVH+Aq4oipID6Aq0UVV1m2XbXkAVoC8wwpLNX1XV+ZbyL4HegBvwpy0bIDYsEvtEI7wajRaTpaP4dsuqOOXPSc21w3EulBdjTBwRd/7hnv/Z1Kr7zwkLDcfF4GJ9rNVqiY83fxo/NDTMOloPYDA4ExwcQmhYOAaDM1FRURgMLgQHhz5V7xOjRg5g5lcLWPz9SsqWLcOanxfhUb7OK+d11jkQER1nfWw0mbC3XFAPX7nL/fAotg40T2HovXwf7oXzUvYt89SjxT61ufFPCP1W+LNlYNNXzpBYaFgYBpeE27VJ2i8sDBdDQtnz2soWbH08v13wPxZ/v4qtm1dSo3pl/PcfSZ+cmoScYaHhGBKVGVye3W4ajYYpk0dQsmQxWrfpkaZcocn2rdEkbj9zOz3h4uKcaq7g4BAGDx7P6tUL+f33e5w+fZ779x+mKVti0eGRODonuq5pE65rTwQu283JH/fRZtkQinz4DrcCLgKwqu1kchcvQOulg/m22kCbZUrI5fTMXCeX7eb0j/totWwIhT8sw7vNvQi795D3W9fAOW922qwYyqpWE22aC+BxeCROLkmzGRNlc9A50G9GfyIjIlk4YoHN9/8skeGPcUrSbpqnsn06ox+R4ZEsGbkwybYT246mYPE3GbJ0FJ9X62nzXHqXZ+fqa2mzRa+5zTLza7s5W0K7kShbsZZVyJY/J3XWDse5UB6MMXGE37n/3+t3ZMJ5+Okp04z4AzuAw8Bpy7ScqcBt4MmQp6ooSriiKOHAA8AZUFRVfQwsAD5WFGWhoih+mN8MANgBp4FVmO8QBCmKMgeIVFX1L8x3AOyAgCchVFU1AkeAdxNlu5Ko/Mmrp80/1fXP8SsUtHyILLdHCYIv37GWnZ74E7s/GsO+TyZxY80B1EXb/3sn33McDjhOg/rmDxdV8vTg/PlL1rLjx09TpYonOp0OV1cDpUuX5PwFlSNHjtOgvvnmTv163hw6/Fuq9T96FEKIZZT577/v42pI2wcK3Qvn5dBV86jS2Tv3KZkvh7XM1ckRnb0djvZadA52GPSOhEXF8sP+82w5fR0wTwfS2nD+ZMCRQOpb2s/T04PzFy5by44fP00Vr0qJ2q8EFy6oNtt3Smx1PEuVKs7aNYsBiI2NJTo6GqMNb68eCTieqN3KcSFxuwWexsvL84Xbbf78qej1Oj5p6Wud8vOqAgICqV/fO8VcgS+Ry87OjkqVylGr1if4+g5AUYoTEBCYpmyJ3Qm8QnFv83WtYLkS/KMmXNdyFSvAxws/B8AYG098TBwmo5HKvZvwXvMqAMQ8jk7SgbOVPwKvUNz7fUuu4k/lar6wf6JcsZiMJhZW/4If20zixzaTiPgnhNUdp9k8F8ClwIuU964AQKlyCrcu30xSPuL7Udy8dIMFX8636XP9RVwJvIy7d3kASpQrxR31VpLyLxYP59bFG/wwfAEmS7amvT+mSvMaAEQ9jsYYb/uvT7wUeClJm92+nDTXl9+P5OalG3yXAW2WmV/b/z5+hTdrms+DPB7Fk2Q7NXE1Oz4ay+5PJnF9zUEuZcF+x39RphnxV1U1UlGUOkBloDHwEdAf6ADEYp5mk/zG0kNFUZyB34BIYD2wCQgH/C31moAOlrsATYGGQG9FUboCqd0/1pD0TVFKkzlt/omW37cHkr9aWWpvGoMGDUcHLkTp0YDwm3/xx66Tz6/gP27Dhu3UrlWNg/s3mr9ZovsAPu/fg2tBN9iyZTfz5i3B328dWq2WUaOnER0dzeQps1n6wyy6+bbj/oOHdOiY+rcjjBk7g0XfzaDXp51xcHCgZ6/Bqa77ImqWKcTRoHt0WrQTgHHNP2DF4UsUymWgRpm3OBr0Jx0X7USr0eBeOC8fFs+Pkj8Ho34NYP2JIIwmE+Oaf5CmDIlt2LidWrWrst9/AxqNhu49BtK/f3eCgm6a22/+Evz2/YpWq2X06OlEp2H++QvlsdHxfPw4krNnL3L44GZMJhM7du7jwMGjNsu5ceMOateqir/fejQaDT16fEH/zyzttnU3879dwr69v6LVahg9JvV2c3d/j65d2nDo8DF27vwZgHnzlrBp045XzlWrVlX8/NZZcg3is8+6ERR0i61bd/Ptt0vZu/cXtFotY8bMSDVXfHw8MTGxBARsJSoqmtmzF/PgwaMU130V6o5AilUpS+d1Y0CjYcughXh2a8Cjm39xdc9J/r54my7rx2HCRJDfGW7/dpkHQXdp/FVP3FtXR2OnZcvgRTbLkzhX0Srv0WHdaDQaDVsHLaKiJdc1S66O68cCJq77neHOb5efV6XNHN0RgHvVckxbNwM0GuYMmkWTbs3489ZdtFo73q30HvaODnjUMHd0V0xbhnry9eQ7vuMoZau8z7h1U0EDCwfNpWG3Jvx18x4aOy1lKr2Lg6MD7jXMbw5WT1+B/5o99PqqP96ta6O10/Ld4LnP2cvL+21HAO5V3Zmybrr5G68GzaZJt6bcu3UPrVbLu5Xew8HRAQ9LrpXTlqGeTN/BjScy82v7ne2BFKj2HvU2jQY0BAxcRJkeDQi7+Re/Z5V+Ryach5+eNOk9t+1FKYpSA6iiqurERMsOA6GYp/SUU1X1tGW5M7ASmAbkBtYB2VVVjbKUt8I8r78Y4Ap0VlV1YKJ6fwRyAJ8AwUA7VVV/sZRpMH9GYJuqqkMURfEHAlVVHZRoexPQWFXVLS/yu/1UsH3maORkPjk7IaMjPJNTwaoZHSFFYSs/zegIqcre0fYdJFswZpLrTErs0vEbUNJCY/uxBZsZlS9znpsAdpm43QJMwc9fKQNk02SaMcCnRJky7x9XahVn+68ltYX4THwOdLi7MtOFizq8Kl1foPRe7TPV75yZzvbHwBjL/PtdQGmgDPAF5hH35Yqi9AH+ASZh/iDvZct6jkBrSyfdA5hlqVMHPAR6KYryCPObhYKWbVeoqvpYUZS5wDeKojwGrmOe218MWJzuv7EQQgghhMg4WWzEP9MMdamqegzwxfytPirmjvdXqqouBToDgcAG4DiQHaijqmqwqqpHMX8IdypwERgNDAIeAeVVVb0DtACaY/7Gnl8t9Uyy7PpLzHcHlgIngbKAt6qqV9P7dxZCCCGEEOJ1yUwj/qiquhxYnsLyYMxfuZnadpOByckW/5iofDuwPZVtYzC/UUjxz/apqlojhWWZ6raNEEIIIYR4eaZMPJ0sPWSaEX8hhBBCCCFE+slUI/5CCCGEEEK8Nllsjr90/IUQQgghRNYkf8BLCCGEEEII8V8jI/5CCCGEECJrymJTfWTEXwghhBBCiCxARvyFEEIIIUTWJHP8hRBCCCGEEP81MuIvhBBCCCGypiw2x186/q9Bs6UfZnSEFMWd2IZ9+YYZHSNVkXcPZnSEVMXtW5nREf5VMvOfuo7PxBf9HHrnjI6Qom8eHc/oCKkymkwZHSFVBbLlyugIKboWcjejI6SqXX7PjI6QKu8Sf2R0hBRFh0vXTqROnh1ZnFPBqhkdIUWZudMPYOiwMKMjPEWryczda/GyMmunXwgh/lNkjr8QQgghhBDiv0ZG/IUQQgghRNaUiad7pgcZ8RdCCCGEECILkBF/IYQQQgiRNcmIvxBCCCGEEOK/Rkb8hRBCCCFE1iTf6iOEEEIIIYT4r5ERfyGEEEIIkTXJHH8hhBBCCCHEf42M+AshhBBCiKxJ5vgLIYQQQggh/muk45+JGI0mJv7sR6ev1+I7Zx23/wlOUr5s70naTl9Nu5k/s+9MEABLdgfiO2cdvnPW0WraT9Qa8UOaMmg0GubPm8qhA5vYu3stxYsXTVLu69OOowHbOHxwM40a1gYgd+6cbN/6I/771vHjqgU4Oemt6+fJk4tLFw6h0+kAcHU1sGXTCvz2/srO7avJly9vmvK+iLMXLtOl75B03ce/od00Gg3z5k3hwP6N7N71dEYfn3YEHNnKwQObaNiwljXj1q2r2Lf3V1at/NaaccCATzkasI0jh7fQtEn9l87yJM/8eVM5eGATe57RZocObqZhojbbtvVH/PatY1WiNvvm6/H8dnQ7e3avZc/utbi6GsiZMwf37p6zLuvX1zdNOW1xbPt/1p0jhzZz5NBmRo0c8Ep5Euea+c04tu/5mY1bV/B2scJJyjt2bsUe/1/ZsXcNdevXSFL2YeUKnLm43/q4ZZum7D+yic07fqR9x0/SlMvW2T5p1Zh9B9az2/9Xuvq2tUm2r2aNZ+feNWzatvKpbJ26tGLv/nXs2reWuvW9k2bzqsi5Swesj8t5lGXrzh/Ztusn/rdiLjqdY5rzPck4evpQVm39nqXrvqVw0beeWidn7hxsC/gFx2T7rNWgOtMXjLdJjsR55s2dzH7/DezatYbixYomKffxacuRw1s5sH8jDRvUSlLWr68vEycMsz5u1aopBw9swt9vPfPmTkaj0dgsY6dJPRixbjJDV4/jjSL5k5TX9f2IkRumMHLDFJr2b2ld/vXRRQxdPY6hq8fxyZD2NsmSQjhcvxhIrm/nk2v2LOzefDNJseGzfuRevJBcs2eRa/YsNM7O2BXIT665s8k1dw7ZR44Ay2tBemTLNbw/+f83h3yLvsK+UMEkxTkH9yH/qm/Jt+gr8i36Co2LMzkH9bI+LvjrEvIvm5s+2V4XozF9fzIZ6fhnIn7ngoiOjWP5wJb0b1yZr9cfspaFPo7mpwNnWD6wJd/1bsqMdQcB8KlTgR8+a8EPn7UgXw4XJnSonaYMTZvWR6/XUaVaE4aPmMKM6aOtZfny5aVvXx+qVW9Gg0btmDhxGI6OjowcMYCfVm+gRs0WnD59nh7dOwJQt051tm/7iXz58ljr6NypFecvXMa71sesWbuJQQN7pSnv8yxZtZYxU2cTEx2Trvv5N7Rb0yb10et0VKvelBEjpzB92qikGfv4UL1Gcxp91J6JE77E0dGRESMGsHr1BmrW+pjTpy/QvXsHsmd3pU9vH6pWa0rDRu2ZOXNsmtqsarUmjBgxhemptFnDRu2YlKjNVq/egHeyNitXriwNG7Wjdp2W1K7TktDQMMqVK8vPP2+wLps779XeFNvq2L79dmHatm1OlWpN8arahDq1q1O2bJlXygTQ8KM66PQ6GtRuzfixMxk/KaFz9cYbeejesyMN67ahZXMfRo75AkdHBwAKvpmf3v18sHcwz/TMlSsnw0d+TtNGHWnSoD2ftGpMocJvprjP150NYNzEobRo2oWGddrQq58P2XO4pilbo8bmbPVqtWL8mJlMmPxlkmw9enaiQZ02fNysK6PHfoGjo7lj/eab+emTLNusuZPo22sYDeu2Ze+eA2lutydqNaiOTudI+0bd+GbStwwe1z9JuVeNSiz+eQ658+ZKsnzYxIF8PqI3Gq1tX9qbNKmHTq+neo1mjBw5hWnJrh19evtQw7s5HzXuwIQJ5nNAr9ezdOlsevbsbF1Xr9czbuxg6tZrRQ3v5rhmd7W+WU4rj7qeOOgcmdRiOGunraTNyIT95i2Ujw+aVmVSixFMaj6cd6u681bpIrxRJD+3zt9gWpsxTGszhl+mr7JJluR0VaugcXTkYe8+hC1chKFP0uu3Q6lSPBw0hIf9P+dh/88xRURg6NWLxxs38bDfZ8ScOo1z61bpks3J2wuNoyN/dvmMR3O/J+eAnknKHUuX5O8+w/irxxf81eMLTOERPJq5wPy49xCM4RE8mPB1umR7bUzG9P3JZKTjn4yiKN8qinIw2bJOiqLcVBTllqIo0xVFua0oyh1FUXLact+ngu7hVaYIAG5v5+fCnb+tZU46ewrkNBAZE0tkdNxToyR7z1zD1UlHZcv2r6pKZU927vID4LdjJynv4WYtq1jRnSNHAomJiSE0NIygoJu4lS2Dl1fCNjt2+lGrZlUAjEYj9eq34eHDhDsX589fxsXFBTCPYsfGxaYp7/MUKliAWZNHpus+4N/RbpW9KrJrlz8Ax46dxMPj/aQZA44nyVi2bBm8Kidss3PnPmrWrEpExGNu3/4dZ+dsODtnw/iKFzavl2izaym02c6dftSsWRWNRkOJEm+zYMF09vtvoEvn1gB4eJSlXLmy7N3zCz/9tJD8+d94pZy2OrZ37tyl0UftMRqNmEwmHBzsiYqKfqVMAB98WJ59e8yXqhPHz+Berqy1zKO8G8eOniQmJpaw0HBuXL/Nu++VRqdz5KtZ4xk8cJx13SJvF+L8uUsEPwrBZDJx6uQ5KlR0f+VctswGcOGCiqurAZ3eEY1Gg8lkSnu23eZR+8Djp3Ev915Ctgpu/Hb0JDExMYSFhnP9+i3efU8xZ5s9gcEDxlrXLVHybR4+fETP3l3YvH0VOXLm4NrVG2nKZs1R6X0O+R0F4OyJ87z7fukk5UajCd+WfQl5FJJk+enjZ5kwdLpNMiTmVdkz0bXjFB6Jz4EK7gSkcO3Q63WsWvUrU6cljAZHR0dTvUYzIiOjALC3syMq+tXPgcRKVizDuf2nALh+6ipFyxa3lj28d5+vO0/EZDn37OztiI2OoWjZYuTMn4shP41jwNIR5C9WMLXq08SxbFmifzsGQOzFizgoSkKhRoPdW2+SffAX5Jo/F6eGDQCwL1qE6KO/ARBz/hyOZcs+Va8t6N3fI/LIcfN+zl3C8Z1SSbI5FH6T3CMHkG/JLJybJr27a2jdjMijJ4i9ZpvnvXg95MO9T1sJHFIUpZCqqncsy9oBP1r+7Q7UA4yqqj6y5Y4jomJwcUq4bWun1RAXb8Tezvz+LF9OAy0mryLeaMKnTvkk2/6w+wRTO9dLcwaDqwuhIWHWx/HxRuzs7IiPj8fV1UBoaKi1LCwsguzZXXE1uBBi2SYsLJzs2Q0A7Nl7kOQePHxEndrVOHvGj1w5c1CjZos0Z36WOt5V+OPeX+m6D/h3tJurwUBIaOKM8QkZDYYk+Z/kMbgaEjKGR5Dd1Zzx99/vceb0Puzs7Jg+Y95LZwFwdU34/c15krZZSKI2Cw+LwDW7K4YU2szZORvzv13KrFkLsbOzY8/utZw4eRZVvcbJk+fYt+8gbds2Z9asibRp0+Olc9rq2MbFxfHggfmSMX3qKE6dvsDVq9dfOo81l8GF0FSOp8E1aVl4eAQGVwPTZo5h/pwf+DPROXE96CZKmZLkzZub8PAIqlX/kKBrN185ly2zAVy+eJW9B9bxOCKSLZt3JTkWtshmTHQ8k5eFh0Xg6mpg+ldjmDf7e+4lypY7d048K3kwbNAEgoJusnrtIs6cOs+B/QFpygfgbHAmLDQ8xYwAAQeOpbjdjo17qFjZI837T858DiQ8z+ONSY9n4vM4LNz8fA8ODmHPngN07JgwrcZkMvH33/cB6N2rCy4uzuzZkzB1Ki2cXJyIDHtsfWyMN6K102KMNxIfF0/4I3PG1sM7cfviDf66cY/seXOy5dt1BG4LoGSF0vT4pj/jmw61SZ7ENM7OGCMSjidGI9jZQXw8Gr2ex+vWE/HzGjRaLblmzyL2skrs1WvoqngRtWMnei8vNHp96jtIU7ZsGMMjEhbEG8FOC/FGNE56wlZvIHTVL6DVkm/RTGIuqsRevQH29hg+/oh7nfqmS67XKhNOx0lPMuKfjKqqR4AbQGsARVHyArWAJ/cAf1ZV9ZiqqoG23rez3pGIqISRXKPRZO30H754i/shEWwd05kd47rgf/Y65279CUDQvYcYnHQUzpsjzRnCQsNxMbhYH2u1WuuLTWhomHXUGcBgcCY4OITQsHAMBmfLMheCg0NJzaiRA5j51QLc3vemQaN2rPl5UZozZwb/hnYLDQvD4OKccsawMFwMCWVP8oSFhiVkdHEmOCSU+vW9yZ//DUoplSleohJNGtenQoWXHyEODQ3H8Iw2MyRqMxeDMyHBIYSl0GaPH0cyd+73REZGER4egZ//Ydzc3sHP7zD+/ocB2LBhO+7vv/vSGcG2x1an07Fi+TxcDC707fclaREWFo5LKsfTnDmhzMXFmdiYGD6oXJ7Bw/qycesKcubMzuKl3xASHMqoYZNZunIec76dwtkzF3n4IG1jGrbK9s67CnXq1cCjbE3KvedN3ry5adLs1T5T8kLZkpW5GJyJiY3lg8oVGPJlPzZtW0nOnNn5fuk3PHwYzI3rt1DVa8TFxbF3zwHeT3T3IC0iwiJwdslmfaxJlDEjPHUOaJIez8TnscHl2dcyjUbD1CkjqVWrGq1f4Y14aiLDI9E7OyXsR2vu9D9hr3Pg09mfo3dxYvnIxQDcPHuNU7vNo91XAy+TI3/SqVO2YoqIQJst4Xii0YKl/UzR0Txe+wtER2OKjCT65EnsSxQn7Ntv0XtVJueM6ZiMRowhIanUntZsj9E6J8qm1Zg7/4ApKprQn9ZhiorG9DiSqOOncSxlvpOir+RB1MlzmBK/aRD/CtLxT9lKoI3l/62BC6qqXrA8DkqvnboXK8ChizcBOHvjT0oWzG0tc82mQ+doj6O9HToHewxOOsIem+et/3blDlXSOMXnicMBx2lQvyYAlTw9OH/+krXs+PHTVKniiU6nw9XVQOnSJTl/QeXIkeM0qG/+QFf9et4cOvxbqvU/ehRiHcn9++/7uBoMNsmd0f4N7RZwJJD6loyenh6cv3A5aUavSokyluDCBZUjAQnb1KtXk8OHjvHoUQiRUVFER0cTHR1NSEgIOV5h3vWRNLZZPUublSpVjP3+G9Bqtdjb2+NV2ZNTp86xcOFMWrRoBEDNmlU5eercS2cE2x7b9b8u4ezZi/TuMxRjGkeZfjt6gtp1qwNQvuL7XLx4xVp28sRZPviwAjqdIwZXF0opxc3LytenaaOONG3UkUePQujedQB2dnaUr+hO4/rt6N1jCCVLFeO3oyczRbbQ0DAiI6OIiozGaDTyzz8PyJEje9qyBZykdr0aAFSo6M7FC2pCtsCzfFg5WbbAM1TyqEeThh1o0rADjx6F0K3rAG7euIOzczbrh4M/qFyRy5eupinbE6eOnaVarcoAuJV/j6uXrtmk3ld1JOB4omtHOS4kvnYEnsbLy/Opa0dq5s+fil6v45OWvtYpP7ZwNfAybt7/Z+++w5q6GjiOf2bLARIAACAASURBVBOQBBnOapejtnpdKKDgHoCr1lGtWvcAZ1117721rXtXbZ24UZkynajgrOtad/tqh4slhhHeP4ghIKgsSeV8nodHk3Nz7y/n3pycnJx7k/xtRzm78vwp30tVPnz9eP64dpdfJ64lSffaa/t9J5q5JrcRpSqV4cmDRzmWx1Dc5cuoatcGoEDlyiTcTvmmz6TUpxRduRyUSjAxwayaDQk3fsesZk2iN/3K0zFjISkJTXiOjzUCoLlwBfN6jgCY2VRKNW3HtMynfLhxSXI2UxNUtlWJ0x3j5rXsiT2Z/jdP/zn57OReMdUnfVuBqZIkfQ50IWW0HyA2tzbqXO1zTsl/0POn3QDM6ObClqDzlPqgEI1tynFK/oMeP+1GqVBgW+4j6lQsBcDdv59SW/f/7PLw8KGJS0OOHTmAQqHArd8Ivh/en5u37uDp6c+KFRsJCd6HUqlkytQFaDQa5s5byqYNS+jr1pVHj5/QvcfgDNc/bfoi1q1ZxKABvShQoAADB43Jkdx57b9Qbx4HfHBp0oAjIR4oFAr69R/J8OH9uHXrbnLGlRsJDtqLUqlk6tSFaDQa5s1byoYNS3Bz7crjx0/o0XMIz5/Hcjb8IsePHUKr1XLiZFiWvq5/WWdHdXXW9zV1NtWgzjZuWIKbrs569BjM8+exbN+xjxPHDxEfn8DWrXu4evUGkybNZf26nxg4oCcxMbEMGDg60xkNc2Z337Zt24KGDWujUpnRonny1WImTZ7PqdNns5TL65A/jZ3q4e3vjkKhYOigCQwa3Ic7t+/h6xPE+jVb8PTbgVKhYM7Mn9BkcIJ7YmIi8fHxBB7dj0ajYdXyjTx5kr0R/5zK9ucfD9i8yR2vwzuIi4vn7p377Ni2L1vZPA8dprFzPXwDdqJQKBgyaDzfDenD7dv38PUOYt2azXj57UCpVDJnxuIMs8XHxzNs8ETWbfgJhUJB2Onz+PuFZCvbSwHeIdRp5MhWz/UoFAomD59FrwFduH/3T4L9Xp0KmNsOHPCliUsDQoL3o1Ao6N9/FMOH6doOL39WrtpIUOBelEoFU6cltx3psbWtSp/enTl+4gx+fjsBWLFiIwcP+mY74zm/01RpUI1Je+eAQsGGMStp5taaf+49RGmiRKpVGVMzU2wa2wGwZ+E2vFbtp/+S4VRzroE2IZGfR2dt2uKbaI4eQ1WzJkVXrQAURMxfQMFOHUn83//QnDjJC/8Aiq1eRVJiArG+h0m4exeFuTmFxo8jKT6OhDt3iVy8JFeyPQ8+jrq2PSU3LUWhUPBo+iKsun1Dwh8PiD0aSoxPIB/+upykhARiPP2Jv538gcq0TCkSPP1zJZOQuxTZPVHqfSVJ0mnAHxgHfCbL8p+SJN0FfpBlOVOtQ6zfCqOtZKvW8/I6QrpiH7z7N7fMMP+4QV5HeIUyhy6LlxuMuZ0x1mSF1RZvXkh4hdaIj7WPCubOVJLsuhnxIK8jZKjrh455HSFD80o/zusI6dJEG++YbplzAUb3RhW7c0auNhrm304zqucspvpkbAswCjguy/KfeR1GEARBEARBELJDdPwz5g6YkXqajyAIgiAIgvC+EHP8BZ2SQByw5+UdsiyXzbM0giAIgiAIgpANouOfhiRJJYCGwFBguyzLz97wEEEQBEEQBOG/yAhH5XOTmOrzKktgI2AOTMzjLIIgCIIgCIKQI8SIfxqyLN8GMn9RckEQBEEQBOG/JUmM+AuCIAiCIAiC8J4RI/6CIAiCIAhC/mREc/wlSTIFfgS6ktxH3wKMlmX5lV8SlCTJDJijW7YgEAIMk2X5j9dtQ4z4C4IgCIIgCELemwt8CbQBvtb9OzeDZScD7YHOQH3AAvB40wZEx18QBEEQBEHIn5KScvfvLUmSpAa+A0bJshwqy/IRkq8wOVCSJPN0HtIG+FmW5WOyLF8BpgP2kiQVe912RMdfEARBEARBEPKWLcmj9kcN7juiu882neWfAO0kSfpQkiQV0BO4CTx93UbEHH9BEARBEAQhfzKeOf6fADGyLEe8vEOW5UhJkp4Dn6az/ChgL/AQSCS5w99AluXXPiHR8c/PIp4QtXVAXqdIV0LQVkydu+d1jAzFPjiW1xHSZfVp47yOkC4tRtOwvioTX8W+S1ojzQXgUrRyXkfIkCmKvI6QoQSMc59WUJXI6wgZOvfiYV5HeA2zvA4gvF8KApp07tcAqnTuLwf8Q/J0oMfATGC3JEm1ZFl+ntFGRMf/HbjsGpjXEdJVdbFdXkd4LfOPG+R1hHQZa6dfEARBeHd+uP9RXkdIV5wRD7Qsy+sA6TGeEf9Y0u/gq4BUHXlJkqyBX4H2siwf1t3XAbgPdAJ+yWgjYo6/IAiCIAiCkD8laXP37+39CVhIkmT18g5dB78g8L80y1Ykee7/xZd3yLIcCdwg+ZuADImOvyAIgiAIgiDkrYtADGA43aGR7r6LaZZ9oPu3+ss7dFcFKgfcet1GxFQfQRAEQRAEIV9K0hrHuTeyLMdKkrQeWCFJUi9AASwHVsqy/EKSpEKAmSzL/8qy/KckSQeApZIk9Sd5jv9UIBrY/brtiI6/IAiCIAiCIOS98YA5cAhIALYBk3RlS4HGQFnd7R4k/7jXDpKn/RwDGr/uxF4QHX9BEARBEAQhvzKek3uRZVkDDNT9pS3rneZ2FMlX9BmamW2IOf6CIAiCIAiCkA+IEX9BEARBEAQhf8rclXf+88SIvyAIgiAIgiDkA2LEXxAEQRAEQcifjOSqPu+KGPFPhyRJZSVJamtw+64kSUPyMpMgCIIgCIIgZIcY8U/fJuAscCDPEigUlJk3gIKVy6LVxHN3zEo0d//SF5ee6YalQyUSo2MBuOk6j8So117BKVu02iTmep7hxl/PKGCiZNrXtSldTP/jcvx6/Cq+v91DoYC+DaviXLkUsXEJTNh9gohYDeYFTJndoS5FLdRZzqBQKFixfB7Vq1VGo9HQf+AYbt26qy93c+1Kv37dSUxIZO68pXh5B1CsWBG2bl6JubmaBw//xq3vCGJjXwBQvHhRjh05iK29CxqNBmtrK7ZvXYWFRUHi4uLp2Xsof//9b5bzvo1LV67z0+qN/LJiYa5uB5Lrb9myOdjYVCIuLo6BA8dy+/Y9fbmraxfc3LqRkJDA/PnL8fEJ1JcNGeJGyZIfMGXKfAC6dm3PiBEDiIyMYsuW3fzyy84s5Vm+fC7VbCqj0cQxcFDq/enq2pV+fbuRkJDIvPlL8fYOpFixImzevAJztZqHD/+mb7+RxMa+YPSo7/j227ZERkbx40+r8fYOpEiRwly5fJQrV2QADhz0ZcWKDW+dbcXyeVTTHWsDMjjWEnTHmrfuWNticKz11R1ri3+aSd26DkRFRQPQ/htXihYtzMYNS1AoFNy//ycDB43VH5eZqb8fFs+gqk1FNJo4hg+ZyJ3b9/XlPXt3olefziQmJvLDwlUc9g3Wl9Wp58C6n3/EplLDVOtcvGwWT59GMHPaD5nK8qacfWcPpGzlssRr4lkzbgV/3Utpy75ya0O91sm/V3MuOJw9S3dS0KogQ5eMoKBlQUzNTPl11kZunJNzLNPLXK6zB1C6clkSNAmsG7eCvw1yfenWmrq6XBeCz7J36U5U5iqGLBuJZWErNM9fsHLEEqKeROZorpfZjLHOXmYbMGcQZSt9RnxcPCvHLuevew/15a3d2lK/TXK2s8Hh7Friri+r1bw2db+qz+JhOXd8GeaaNH80FaqUJy4ujhkj5/HH3dQ/dlqkWGF+PbSWDk49iNPEYWllwdyV07CwsqBAgQL8MG0Zl85ezpVs7We78lGl0iTGJbBr3Doe3/tbX97A7UvsWtcF4FrwBfyX7sW8kAVdlwxGbWlOzLNo9oxfT/Tj3DnWOs5245NKZUiIi2fHuLU8MsjW2K0l9rpsV4Mv4Lt0D2orc3ovH46ZuZrE+AQ2j1hO1L8ROZ7tnTGiq/q8C2LEP32KvA5QpEUtlKoCXGsznj/nbaHU1D6pygvafM6NrjOQO05B7jglVzv9AMHX/kCToGVz/+YMb2bHT77n9GWRsXHsOCWzuV8z1vRyZpF3OAB7w29S6eOibOrbjOY2ZVgfkr0GtW3bFqjVKuo3bMPESfNYtHCqvqxkyQ8YMsSVho2+5suvujJ79njMzMyYPGkEO9w9aOzcngsXLtO/Xw8AmjVthI/3DkqWLK5fR6+enbh85TpOLt+wa/dBRo8clK28b7Jx226mzV9KnCYuV7fzUps2zVGrVTRu3I7Jk+ezYMEUfVnJkh/w3Xd9cHJqT+vWPZg1axxmZmao1So2bVrCwIE99csWK1aE6dNH06xZJ5o06Ujnzl9Tpsynmc7Ttk0L1CoVDRu1ZdLkeSxMk2fIYFcaNW7HV626MXvWBMzMzJg0aQTu7h44u3zDhQtX6NevO1WrVKRz56+p36ANLb/qxrSpozE3V2NnV5Wduw7QtFlHmjbr+Nadfkg51ho0bMOkSfNYmMGx1vKrrswxONbc3T1wSnOs2dnZ0PKrrjRp2pEmTTsSGRnF/PlTWLd+C07O7TlyJJTvvx+Q6fr7qnVTVGoVzV06MXPaD8yaO0FfVqJEcfoP7MmXTTvzzdd9mDp9FGZmZgB88smHDB7qimmB1OM+vfp0pnIVKdM53sSheS3MVAWY1G4c2xZspudk15ScpUrS4OtGTG4/jkntxlK9oR2lK5ahVd+2/HbiEtO+ncTKUctwm5X5+nmTms1rUUBlxrR249mxYDPdJ6e0sSVKlaT+142Y2n48U9uNw6ahLaUrlsG5SzPu/HaLGR0ncvLQcdoN7ZjjucB46wySO+8FVGaMbzeGLfN/pc+UlGwlS5ekYbtGTGg3lvFfj8G2oR1lKpYFwG16P7qP64VCmTtvr85fNsRMbUbPVv1ZOns1o6YPS1Vet3Et1rgvodgHRfX39RjYmdPHwnFrN5gpw2czcd6oXMlWpVlNTFUFWNF+Gl4LdtB6cnd9WdFSJbBvW5/l7aeyvN1UKjSw4aOKpXEZ/DV3wmRWdpzBiV/8+HLMt7mSzaaZAwVUBVjcfgqHFuyg3eQe+rJipUpQs219FrefwuJ2U6jYoBofVyxNrQ6NeXD9D5Z9O51znidx6d8mV7IJueO96PhLkpQkSVJnSZIuSpL0QpKk47rpOislSYqUJOlPSZJ6GCx/V5Kk4ZIkhUiS9FySpEuSJLXWlf1C8k8kj5Ik6a7BZj6XJClQt/5bkiTlTouvY+lYiYjg8wDEnLuBRbXPUwoVCtSffUTZhd9R0WMuxb91yc0oAJy//y/1vvgIgGqlinPlf4/1ZeZmpnxU2ILY+ARi4xJRKJIb9u51K9K3URUA/op4TjHLrI/2A9Sv64jf4eRRy9NnzlHDvpq+zMHBlpMnw4mLiyMyMopbt+5SzaYS9eqlPMbXLxgX5+SRKK1WS/MWnXny5Jl+HZcvX8fS0hIAa2sr4hPis5X3TUp9/BFL5k7O1W0YqlvXgcOHQwA4c+Y89gb1V7OmLaGhqevPxqYiarWabdv2smDBcv2yn31WmosXr/L0aQRJSUmEh1/C0dEu83nqGeY5h729/pfHk/dnaFiaPJWoZ/Ac/PyCcHZuQMWKX3DkaCgajQaNRsPNm3ewsamMvV017GyrEuC/hx3b1/DhhyXeOlu9TBxrN9M51vz8gnF2boBCoeCLLz5j9eqFHAnxoHev5DfvSpXK46sbgT8ZGka9ug6Zrr/adWoQ5H8UgPCwC9jaVdWX2desxulT54iLiyMqMprbt+9RpaqESmXGj0tnMWbE9FTrcnC0xcHRll82upPTKjlU5vyR5Lbs9/M3+LzaF/qyxw8fMafndLRaLUlJSZiamhCvicdzw0ECtvkBoDRREq/J+dei5FCJi0eSBzBunr9BuTS55vecQZIul4mpCXGaeHw2HmL/ij0AFP+4OBGPcmeU01jrTJ8t5CwAN87LfF6tvL7s0YNHzOxhmM2UeN3AxvWz11g7aVWuZAKwc6zOyaDTAPx27gpVqldMVa7VaunfaRgRz1JGzbeu3cmeLR4AmJqaoMmlQZjPHCTkIxcBuH/+JqVsyunLnj18zPpe80nSJumPtXhNHCXLf8J13WPuhMt85pDzH8oBPneQuKbbzt3zv1PKJqWv8fThY1b3mpcmWzwPrt9HrXs/V1sWJDEhIVeyvTNabe7+GZn3ouOvMx/4HqgNlAbOAZGAA7APWCtJkqXB8jOBNUBN4HdgkyRJZsBwIBRYrXvsS/2BDUAVwB/YIkmSdW49GRNL81Sj+ElaLZgk7y5lQRV/b/Li9tDF3Og2kxK9WmBeqUxuRQEgRhOPpbpASj6lgoTElAO6pHVB2i/zpPNqb7rUkQyWU9JvYwDup2TqV/g4WxmsrC2JjIjS305M1GJiYgIkd9QjI1Ma9KioGAoVssbaypII3WOioqIpVCh5elJA4DGePHmaav2PnzylaZOGXLoYzKiRA9m4Kec7QYaaOtXH1PTdzbaztk6pCwCtNtGg/lKXRUcn19+zZxEEBBxLtZ6bN+9SuXIFSpQojrm5GienelhYFMx8HisrIiIN96dBHiurVPv65b6zsrZK2Z/RMRSytuLyles0qF8LS0sLihYtTO3aNbGwMEe+cZOZs36kSdMOHDzox5LFs94+W5r6SHusRRgca9FRMVgXssYqnWPNwqIgK1dtolevoXzVqhsDB/bCxqYSFy9eoXXrZgC0atUsS/VnZWVJpEH9aQ0ypi2LjorB2tqKhT9OY8XSn3n4MOWr/JIlP2DchGGMGTk90xnehrllQZ5HxaTKqdS1ZYkJiUQ9Tc7ZY1Jv7ly5zcM7D3geGUOcJo7CHxRm2NKRbF+wOZdypbSxGeXqNqk3d6/c4a87D4Dktnjyjpk07/0VF4LP5niulGzGV2cp2V5Xb8mvjV6TXLl95TYPdPV24tBxkpJy7yRKC6uC+ul0kLo9ATh1NIyIp6mnykRFRqN5EUexD4oyd8U0ls1dnSvZ1JbmvMigzrQJiTzX7c9WE7vxv6t3eXTnLx5cvUeVJjUAqNK0BgXUqlzKVpDY12SL0WVrO7E7f169y793HhLzLJqKDaox0f9HXAa05tTO4HTXLRin96njv1KW5WBZli8AnkA0MFGWZRn4ieSfQP7MYPkdsiy7y7J8FZgBFAPKyrIcAcQBz2VZNpzgvUGW5e2yLN8CZgMqIPWQQg5KjI5FaTBCrlAqQNfR1sbG8ffPnmhfxKGNeUHkid8oWLlsbkUBwEJVgBhNyqd6bVISprrG4cSNBzyKfoHXyK/xHdWOkGt/8tufj/TLrndtwsa+TRm949gr682MqMhoLK1SPrsplUoSExMBiIyM0o/WA1hZWfDsWQSRUdFYWVno7rPk2bOM50hOmTyCH35cTbXqTnz5VVd27VyXrbzGJjIyGiuD+lMoDOsvpZ4ALC0tMqyrZ88iGDNmJu7ua1m79gcuXLjMo0dPMp8nKgory5RtptqfUVFYGuR5ue+iIqNS9qelBc8iIrl+/SarVv/CoYNbWLhgKmFh53n86AnBwScICTkJgMcBH6rbVnn7bGnqKu2xZmVwrFlaWRDxLIKodI61589jWb78Z2JjXxAdHUNwyAmqVavM2LEzadWqKZ6HtqLVann0OPP1FxUVjWUG9Ze2zNLKgrj4eGrXrcnYCUM56L2VIkUK8fOmxbRt9yVFixVh596fGT6yPx06tqZLt/aZzpOR2OjnmFuY628rlAq0BoMGBVQFGL5sJOYW5vw8ea3+/tJSGaZun8X2hVu4evpKjuXJTK4hy0aitjBno0EugNldpjKj40RGrBmX47neNlte1NnLbGrL12cbsWw05pbmrJuUOx3p9MREPcfCMuUDtOHr4XW+qFiO9XuWsWzeGs6GXsiVbC+iY1G9Zn+aqgrQdekQVJZq9k3eCEDQqgMU/bQ4/bdOpPBHxXj28PEr682ZbM9RG5x7p0wnW8+lQ1FbmrNr8s8AfDm8AwFrDzG36ShW9ZiD6+qRuZLtnUlKyt0/I/M+dfxvGvz/OXBXluWXNf7yrDnDj8w3DP7/sodTgIzdMvj/y/kh5uktmBOiw65R2Dn5076FfQWeX0s5aU9d7mMqecwFpRKFqQlWjpWI+e12bkUBwLb0Bxz/PXnk5tIfjyhfsrC+zNrcDJWpCWamSlQFTLBSmxH1Ip4NRy7jeSE5l7mZKcpszu08ERrGly2cAajlaM/ly9f0ZWFhF6hf3xGVSoW1tRUVK5bn8hWZkyfD+LJF8lSoFs2dOH7idIbrf/o0Qj+S+88/j7C2sspw2f+i0NBwWrRwAsDR0Y4rV67ry8LDL1CvnmH9faE/KTYtExMTatWyw8WlA25uI5CkzwkNDc98npPhtNDtT0dHey4b5AkLu0D9erVeyXMyNOUxzZs7c+L4GYoXL0rxYkVxcm7PyFHT+PTTj7l8RWbtmkW0b9cSAGfn+pw/99tbZzuZzWOtue5Yq1ChHEdCPFAqlZiamlKvriPnz/9GkyYNmT17Ma1ad0erTSIwIPMfik+HnqNJ88YA1HSw5arB/joXfok6dWuiUplhZW1JBelzzoVfpJZ9c9q07E6blt15+jSCvn1GsG7NZpwbtqNNy+4s/Wkde3YfYse2fZnOk5Hr4dewd0puy8rbVeC+fC9V+dj1k7h79S7rJq5Gq/ta/NPypRi5aixLh/3IhZBzr6wzJ9wIv46tLtcXdhX4I02uUesncu/qHTZMXJ38jSvQ9rtvqN+uMQAvnmvQvkXHMiuMtc4AroVfo4ZTTQAq2Encv54624SfJ3P32h3WTFipz/YunA+7RH2XOgDY2Ffh9+u33vAIKFehLD+sn8P4QdM5EXQq17LdDb9BRSdbAErbfcFf8h+pyvusH8XDa/fYO3EDSbpLS5ZzrET4vmOs6z6XJ3/8y93wG6+sNyfcDpep7JQ8VbOsXXkeyPdTlfdbP4YH1+6xc+J6fbbnEdH6bzCiHkegtsq1rpCQC96nq/qkndD4phYnvcl8r+uZptfC59pJwE99TmPd0JZKB+aBQsGdEcsp2b8NmjsPeeYfxuN9R6l8aD5JCYk82hPCixt/vHml2eBcqRSnbj2k57rkOaQz2tVmy4lrlCpqReNKn3Lq1l/0WOeHUqHAtvQH1Pn8Q6QPCzNlbyj7z95Cm5TEjHa1s5XBw8OHJi4NOXbkAAqFArd+I/h+eH9u3rqDp6c/K1ZsJCR4H0qlkilTF6DRaJg7bymbNiyhr1tXHj1+QvcegzNc/7Tpi1i3ZhGDBvSiQIECDBw0Jlt5jc2BA764uDQgOHgfCoWC/v1HM2xYX27duoeXlz+rVm0iMHAPSqWSadMWodFo0l1PYmIicXHxhIZ68eKFhqVL1/P48dN0l30djwM+uDRpwJEQDxQKBf36j2T48H7cunU3eX+u3Ehw0F6USiVTpy5Eo9Ewb95SNmxYgptrVx4/fkKPnkN4/jyWz8qV5uQJT+Li4hk/YTZarZZJk+exbu2PDBjQi5jnzxk48O3358tj7ajuWOv7mmNtqsGxtnHDEtx0x1qPHoN5/jyW7Tv2ceL4IeLjE9i6dQ9Xr97A0sKC9et+RKOJ4+pVmaHDJmW6/jwPHaaxcz18A3aiUCgYMmg83w3pw+3b9/D1DmLdms14+e1AqVQyZ8biXJu//CZnfE9Rrb4ts/ctQKGAlaOX0apvG/66+xdKEyWVa1WhgJkpdo3tAdi+cAtfD/qGAqoC9JnWF4DnUc9Z2G9ujuYK8z2FTf3qzNg3HxSwdvRyWvZtw993H6IwUVKpVhUKmBXAtnFyB9x94RZCdgUw6MfhOH3bBKWJkjVjlr9hK1ljrHUGcNo3FNsGtszbtzD5ylyjl9Kmb1se3nuIUqmkSq2qFDArgL2u3rYu+BU5F64ulFaQ9xHqNHTg10NrUSgUTP1+Dj0GdOb+nT85cvh4uo8ZNnEQZmozxs7+HoDoyBi+753z3+Jc9gujQgMbhuydAQrYOWYtDd1a8uje3yhNFJSrVQlTswJUbJz84cB7oTv/3H5Al5++AyDiryfsGpc730Bf8gtDalCNEXtngkLBtjGrcXL7in/vJR9rX9SqhKmZKZV02Q4t3IHXj7vosmAA9bs3w6SACe7j/+PfjhvhPPzcpMjNOXfviiRJSUBrWZY9dbd/AGrKstxYd/tD4CHgIMtyuO6k3R9kWV6hKy8L3AFsZFm+LElSMHBWluXRuvK0y1sCUYCTLMshb8oX9kk7o6zkqoszf0Lmu2TVfe2bF8oDsQ+yN2UpN1l92jivI6RLa8Q/iW6sbaCVKvPz/t8Vl6KV8zpChkzz/qJsGUrAOI+1BCN+fd6Oy50pLjmhqTp3z63Lqrg3jnvmnWV3dxrdC/T5T/1y9YVZcOR6o3rO79NUn5wUDXwhSdIneR1EEARBEARByCXapNz9MzKi45++1UA94KIkSaKOBEEQBEEQhP+892KOvyzLijS3R6e5/RcG8/FlWS6bpvxumnJv4AODRdIuH40R/MiXIAiCIAiCkA1GPNUtN7wXHX9BEARBEARByDQjnI6Tm8Q0FkEQBEEQBEHIB8SIvyAIgiAIgpAvJeWzy3mKEX9BEARBEARByAfEiL8gCIIgCIKQP4k5/oIgCIIgCIIgvG/EiL8gCIIgCIKQP+Wzy3mKEX9BEARBEARByAfEiL8gCIIgCIKQP4k5/oIgCIIgCIIgvG/EiP870ODx2byOkK6n2OV1hNdSKhR5HeE/J+rPkLyOkCGbyt/mdYR01S1YJq8jpGvPv+fyOkKGfP69lNcRMmTM7UZcYkJeR0hXojYxryNkqLC5ZV5HyJCX1jj3Z2R8TF5HyNCyvA6Qnnx2HX/R8c/HCvVYl9cR/pOsPm2c1xHSZcydfkEQBEEQ8p7o+AuCIAiCIAj5k5jjLwiCIAiCIAjC+0aM+AuCIAiCIAj5k7iOvyAIgiAIgiAI7xsx4i8IF/le7QAAIABJREFUgiAIgiDkT2KOvyAIgiAIgiAI7xsx4i8IgiAIgiDkS0n57Dr+YsRfEARBEARBEPIBMeIvCIIgCIIg5E9ijn/ekSQpSZKkVjnxeEmSfpEkac9rlt0jSdIvWd2WIAiCIAiCIPyXGFXHP4cNB/rmdYg3USgULF8+l5CQ/Rw+vJNy5cqkKnd17cKJE54cOeLBl1+6pCobMsSNWbPG62/XqFGNwMA9BAXtZfv2NahUqmxnW7FiHkePHMD/8G4+/7xsmmxdCT3pxbGjB2nZMnW2oUPdmDN7Qqr7zM3VhATvR5I+z1aurGYrVqwIXl7bCArcy7atqzA3VwMwYsQAToV6c/KEJ23btMiRbDm1T7t2bU9YmB+BgXvo3fvbbGfLjEtXrtN7yNh3uk1DCoWC6YvG4+69gc3711D6s09fWaZIscL4ntqLmcrsneTpMac/E/fNYaz7DEqU+TBVeVO3Vkz2mMdkj3m0Gd5Rf/+Pp9Yy1n0GY91n8M3YrjmaZ/HS2QQE7cHLZ/srx1mv3t8ScuwAgcF7adHCGYASJYtz0Gsrvod38svm5frXwOChbpwO88XLZztePtv5ovxn2c62dNlsAoP34uO745Vsvft05ujxAwSF7KPFl8nZPv30Yw55bsHHdwe+fu6UL18OgM5d2nHqtA+H/XfRs1enbOV6mS2n6q1N2xaEHPUg+Mj+bGcztvcChULByhXzOXb0IAH+r7axbq5dORXqzfFjh2jZsgmQ3MZ6e20nOGgf27at1tdT8+ZOHD92iOPHDrF82VwAChY0Z+/ejQQH7cPz0FaKFy+a6Ywvcy5aPANvf3c8PDfzWbnSqcq79+qIf8hefAJ20rR5YwAKFynE9dun8PDcjIfnZvoP7Jlqfe571tPLtXOW8mSUcdqi8ezw3sCv+1e/pi3b80pb1qRlYxatnpVjWV7mmf/TVA76bWP3oU2U/Sx1nXXt2QHvoJ0cOrydJs0bAWBe0Jylq+eyz3szh/x3YGtvA0C7jl/hG7Ibr8Cd9HR9t+9ROUablLt/Rua9neojy3JEXmd4G23aNEetVtG4cTscHe1YsGAKHTsmf14pWfIDvvuuD3XrtkKtVhEUtJfAwGMolQpWr16Ag4Md+/f76Ne1atUCunQZyO3b9+jTpzOlS3/C77/fznK2tm1aoFapaNioLY6O9ixcMIVvOrjpsw0Z7ErtOi1Rq1WEBO8nIOAYSqWSNasX4uBoh8d+b/267O2rsXLFPD755KMs58lutkmTRuDu7sGWLbsZM3ow/fp159dfdzH4O1cqVa6PhUVBws74ceCgb7ay5dQ+LVasCNOnj6ZWrS959iwSH5/tBAef4N69P7NXeW9h47bdHPINwlydvQ+P2dGkZWNUKhWdW7pRvUZVxk3/nsG9RuvL6zvVZuTkIRT/IGsdhsyya+ZIAVUB5rafRDm78nw7uRfL+y0A4INSJajTtgGzvp4ASUmM3z2Lc35niIvVcO/yHZb1nZ/jeVq1boZabUYT5w44ONgyZ95Eunw7AEjuqA78rjeN6rdFrVbhF7CLoKDjjBw1iB3b9rJj+34mTByOq1tXVq7YiK1tFQb0G82FC5dzJFvrNs1QqVS4OH2Dg4Mtc+dPonOn/vpsgwb1ooEum3/ALoICjzNl6kjWrt2M5yF/XJo0ZMbMMQwdMpGp00ZRr85XPHsWiafXVkKCT3D//v/yvN5Wr/qFGTPH0qhBW6KjYwg7exhPT3+ePH6apVzG9l7Qtm0L1GoVDRq2oZajPQsXTuWbb1z1eYYMcaVW7eQ29kjIfgICjjJZ18Zu3rKLMWMG079fDzZs3M78+ZNp0qQDjx8/ZdSoQRQvXpSuXdpz7twl5sxZQs8enZg4YTgjR03LdL21bNUEtcqMlk07U6NmdWbMHk/Prt8BUKJEcfoN6EHTxt+gUqvw9N3OkeATVKtemf17PJkwdvYr65s45XsKFymU6Ryv06RlI1QqM7ro2rKx04czpNcYfXk9p9qMnDyYYmnasgmzR1LfqTbXL9/I0TwtvnJBpVLRpnk37GtWY+rsMbh2GwrAByWK4zqgGy2dOqFSq9jvs4WjwScZNLQP16/dZPigiVSqUoHKVSUunPuNKTPH4FynLTExzwk+dZADe32IiIjM0by5TvyAV56rKUnSGUmSXkiSdFmSpEYvCyRJKiRJ0nJJkv4nSdJzSZJ8JUmS0ltJ2qk+kiT1kCTppiRJMZIkrQfM0iw/QpKkG5IkxUmS9ESSpC2SJFlKkqTS3XZLs3yQJEkzsvtk69Z14PDhEADOnDmPvX21lIqoaUtoaDhxcXFERkZx69ZdbGwqolar2bZtLwsWLNcvW758OZ48ecbQoW74+++iSJHC2er0A9StZ5jtHPb21fVlDg62nAwNS5OtEmq1iq3b9jB//rJU61KpzOjYqS+yfCtbmbKTrZ5BXfv5BeHs3ICYmOfcv/8nFhYFsbAoiDYHGoCc2qeffVaaixev8vRpBElJSYSHX8LR0S7b+d5GqY8/Ysncye9kWxmpUas6x4JOAnDx7GWq2lZKVa7VanHtMJiIp+/mTaa8Q0UuH7kAwO3zv1PWppy+7MnDx/zUazZJWi1JSUmYmJoQr4mjjE05inxYlDE7pvP9pol8WO7jHMtTp25NAvyPAhAWdgE73QgcQI0a1TkVelZ/nN2+dZeqVSsyfuws3Hd4oFAo+OTTj/jnn0cA2NpWZeToQfj572Lk6EHZz1anJgH+R/TZ7A2y1axpy6lTBtlu36OqTUUmTpiDr08wAKamJrx4oaHsZ6W5dCnlNXD2bPZfAzlVb1qtlpr2TYmMjKJosSIoFApiomOynMvY3gvq1XXE73Dy/jh95hw1DPI4ONhy8mRKnpu37lLNphL16qU8xs8vGGfnBtSpU5PLl6+zaOE0goP28c8/j3j06AnLlv/MvHnJ7xOlSn/M37pjMbNq1a5BYOAxAM6GX8TWrqq+zK5GNc6cPk9cXDxRkdHcuX2fylUrUt22KtWqV+GA1xY2/LqUkiU/AKB12+ZotUkE6o6PnGJfy5bjQaFA+m1ZklaLW4chr7RlF8IuMWPsghzNAuBY257gwOMAnAu/RDXbKvoy2xo2hBvU2d3b96lURaKxcz3i4+LZtmcd348eSEjgCQCuXbmBlbUlKrUZCgUkJRnfCLeQmjF2/AcB0wEb4DawXZIkha5sD+AEdAFqAS+Aw5IkFXzdCiVJcgI2Aj8B9kA00NqgvItumyOB8kBvoC0wQJZljW67XQyW/xhoBGzPzhMFsLa2JCIiSn9bq03ExMQk3bLo6BgKFbLm2bMIAgKOpVpP8eJFqV27BmvXbuHLL7vi5FQPJ6d62ctmZUVEZMr2ExMNsllZEWmQLSoqmkKFrHTZXm00Q0PD+fPPh9nKk91sVtZW+vqMio6hkLUVAH/++ZCLF4I4fcqHlSs3Zj9bDu3TmzfvUrlyBUqUKI65uRonp3pYWLz2UM8xTZ3qY2qat18IWlhaEBWZ0pFKTNTq6xHg5JEzPHv67r7YM7c0Jzbquf62NlGL0iS5CU1MSCT6afJ+7TSxJ/ev3uHvOw+J+OcpXqv2s6jLdLxW7qPf4mE5lsfKypLIVK8BbarjzLAsOjoG60LJx7uJiQmnw3xp0LA2p0LDAdi7x5Pvh0+mVctu1KlTUz/FJcvZrDN+fVpZpX4NREXFYG1txePHT0lISKB8+XLMnTuReXOXcevmHSpVKq9/DTR2qkvBbL4GcrLeEhMTad2mOSdPeXHixBni4xOynMvY3gvSbjN1PVkREZnSSY2OisG6kHWqffuy3S1evCiNG9VlwsQ5tGrdnWFD++qncWm1Wg77JX/r6usblOmMAFbWlkRFRBvkTH2svbI/rS35/cZtFsxbRtuveuDtGcC8RZOpWKk87Tu2Yv6cpVnK8TqWlhZERRpmfLu2zOdAQK50pC2tLIiKNDzWtAZ1ZkGkQdYYXZ0VLVaEQoWt6dahP/6+IUydlfztq3ztd3xCdhMceoAAvyOp6vs/I59N9THGjv8CWZa9ZVn+HVgIfAx8IElSVaAJ0FuW5aOyLP8GdAMsdP++zkDAQ5blVbIsyyR38K8blD/QrddTluV7siwfBI4AlXXlW4HGkiSV1N3uDJzTrStbIiOjsbKy1N9WKJQkJiYalFnoyywtLXj2LP3RzcePn3Lr1l2uX/+dhIQEDh8Owc7OJt1l3zpbVBRWlinbVyoNskVFYWmQzcrKMsNsuSEr2aIio/T1aWVpwbOISFq0cOLDD0tQQarL51/Uok3rFtSsaZu9bDm0T589i2DMmJm4u69l7dofuHDhMo8ePclWtv+SmOgYLCxTOnlKpUJfj3khNjoWtYVaf1uhVKJNTPmGyFRVgP5Lh6O2VLNl8s8A3L10i/P+YQD8Hn6dIh/m3LSkqKhoLFO9BhSpjzPL1MdZhO44S0hIwLFmc4YPncTa9T8CsGrlJp48fkp8fDx+vsFUq16Z7IiKjMLKMuU1YPj6jIpK/RqwsrLQTw1o2LA27jvX0rfvSH7//TbPnkUyftxstm5fxeo1C7l44QqPsziVRp8tB+sN4NBBP6Qv6mBmVoAu3dpnOZexvRekzZOqjU2zfy2tLIh4FpFq375sdx8/fkr42Yv8/fe/xMQ859ixU1SvnjLC3Kx5J5yc27HTfV2mMwJERUanau/THmuWafdnRBTHjp7i+NHTAHh7+lO1WmU6dfmajz4qyf5Dv9K5azsGDe6Ns0uDLGVKKzo6BosMjrm8EB0Vk/o1oFAY1FnqMgtdnT198ozDum/k/H1DqGZblUpVKuDSrCF1bJtRq1ozin9QjFZtm73bJyNkmjF2/A3ngjzT/WsOVAHigLMvC2VZjgHO68pepypwzuBxSUCYwe0jwB+SJM3WXe3nGvAV8PIj+THgD+DlGXtdgW2Ze1rpCw0Np0ULJwAcHe24ciXl80h4+AXq1XNEpVJhbW1FxYpfcOVK+p817ty5j6VlQf0JYfXqOXL1avbmBYaeDNeP/Dk62nPZIFtY2AXq16v1VtlyQ1aynQxNeUzz5s6cOH6Gp08jiH3xAo1Gg0ajISIigsKFrbOXLYf2qYmJCbVq2eHi0gE3txFI0ueE6kYa84NzZy7SqEnySGX1GlW5cS1npoll1c3w69g42QNQzq48/5Pvpyoftn4cf1y7x+aJ6/Q/CNPm+040df0KgFKVyvD4QdamM6TnVOhZmulOVnRwsOWqwXF09uxF6tRzQKUyw9raCkn6gqtXZX5aPJMGDWsDEB0VjVarxdrailNhPvpvkxo2qpPtuf5psxke4+HhF6hbN022KzING9Zm4aJpfN22N+fP/QYkvwYcHe1o3vRb+vUdRYUKn+tH23MqW1brzcrKEm/fHZiZmZGUlERMTGy2fgjI2N4LToaG8aWuvazlaM/ly9f0ZWFhF6hf3zBPeS5fkTl5MowvWySfeNy8uRPHT5zm3LlLVKksUaxYEV2bZs+1azcYO3YI3bp9A0BMTCyJWay7M6fP0aRpQwBq1KzONYPnev7sJWrXqYFKZYaVtSUVpM+5fvUGS5bPpnXb5gA0aFSHSxeuMHPqIlq4dOLrVj1x376f1St/ISjwWLrbzKxzZy7SsEldwDjasrDT53HW1Zl9zWpcu/a7vuzC2d9wrGOvr7PyFcohX/udM6fO4aJ7TK26Nbhx/SaRkVG8eKHhRawGrVbLo38fU6hwzp4f8S4kaZNy9c/YGOPJvel9DFYAsRksr+DtPsAo0tyOQ/f8JUnqDawGNgE+wCxAP39fluUkSZK2A99KkuQH2AJZvuyooQMHfHFxaUBw8D4UCgX9+49m2LC+3Lp1Dy8vf1at2kRg4B6USiXTpi1Co9Gku574+HgGDhzLr78uR6FQcOrU2Sx/dfqSxwEfXJo04EhI8tzWfv1HMnx4P27duounpz8rVm4kOGgvSqWSqVMXZpgtN2Ql27x5S9mwYQlurl15/PgJPXoO4fnzWM6GX+T4sUNotVpOnAxLd6pSZuTUPk1MTCQuLp7QUC9evNCwdOn6bI92/pf4e4VQt1EtdnhtQKGACcNm0ntgV+7d+ZNgv5ydg/s2zvmdoXKD6kzcOwcUsHHMSpq5teKfe3+hNFEi1aqMqVkBbBonz0Hfu3Ab3qv202/JMKo725OYoGXD6BU5lufQQT+cnOvjH7gbhULBoIFjGTzUjdu37uLjHciaVb/g578LhVLBzBk/otHEsXr1LyxZOptx44eiTUpi5IipREZGMXP6D3j5bEejieNIyEkO+4VkK9vBg344u9QnIGhPcrYBYxgy1I3bt+/h7RXA6tW/cjhgF0qlkhnTf0CjiWPBwqmYmRVg3fofAPj999sMGzqJuLg4jp88xIsXGpYv/Tnbr4GcqreoqGh27TyA72F34uMTuHL5Ou47PLKcy9jeCzw8fGji0pCjRw6gUCjo228E3w/vz81bd5Lb2BUbCQnep2tjF6DRaJg7bykbNyzBza0rjx4/oUePwTx/HsvkKfPw9kqeGbt7zyGuXJH599/HbNywhD69O2NiYkK/viOyVG9eh/xp5FQPr8M7UCgUDPtuIgMH9+bO7fv4+QSxfu0WDvluR6lUMHfWYjSaOGZN/5GlK+bSx60Lz5/HMmJo7p7PFKBry7Z7/YxCoWDisJn0GtiV+3f+INgvZz5cZIaPZwANnepwwG8rChSMGDKZ/t/14s6d+/j7BLNx7Tb2eW9BqVSwYPYyNJo4lv+0nkXLZnDQbxvxCQkMHziB//3xkK2/7GK/zxbi4+O5d+cPdm3P+mtAeDcUxnQihiRJSUBrWZY9dberAr8BnwEFgBuAgyzL4brygsA9YJosy6sMH6+7Rr+lLMsdJEnaARSUZbmtwbbOAZdkWe4tSVIY4CPL8lRdmQK4DITLstxLd19F3X0LdBne+vsstbq08VSygZw4kTU/UiqM8YsyiPozJK8jvJZNZeO81FvdgmXevFAe2PPvuTcvlEe0RvS+kZZSkXaMx3jEJWb9HIDclKjNu2knb1LY3PLNC+WR4irjHN2OjM/6Sea57X9PrxjdCzRqWKtcbdCslnka1XM2xhH/dMmy/LskSfuATZIkfQc8BaaR/A2B+xsevhQ4JknSSMAT6AlUBy7pyh8DTpIkVSb5m4ERJM/v/81g+9clSbpA8vkBA3PsiQmCIAiCIAjCO2CcQ5cZcwXOAAeBU0BBoKEsy68941GW5VMkz8/vB1wkuVO/w2CR4UASEA4EACpgHslXADL08io++7L1LARBEARBEIS8p9Xm7p+RMaqpPsZOkqRFwKeyLHd548IGxFSf94uY6pM1YqpP5oipPlkjpvpknpjqkzViqk/mGeVUnyEtc3eqzwpvo3rO/5mpPnlJkiRHkn9XYADJV/sRBEEQBEEQ/uuM8Mo7uck4hy6NjzOwDFgly/K7PwVfEARBEARBELJJjPi/BVmW5wPz8zqHIAiCIAiCkIPEiL8gCIIgCIIgCO8bMeIvCIIgCIIg5Ev57SI3YsRfEARBEARBEPIBMeIvCIIgCIIg5E9ijr8gCIIgCIIgCO8bMeIvCIIgCIIg5E/5bMRfdPzfgQQj/lVEY2VUP3OXhhbxi8dZ8dvVnXkdIUPFyzbN6wivUCgUJBrhz70DvEiIy+sIGTLmtsNYf4U2UvM8ryNkyMRIfykd4IkmMq8jpMtUaZLXEQQjJjr+gvCesKn8bV5HyJAxd/qNlbF2+gVBEN4nSWLEXxAEQRAEQRDygXzW8Tfe79AEQRAEQRAEQcgxYsRfEARBEARByJ/y2axKMeIvCIIgCIIgCPmAGPEXBEEQBEEQ8qX8dnKvGPEXBEEQBEEQhHxAjPgLgiAIgiAI+ZMY8RcEQRAEQRAE4X0jRvwFQRAEQRCE/Elc1UcQBEEQBEEQhPfNezHiL0nSL4ClLMsd3nL5PUC0LMu9czOXIAiCIAiCYLzEVX2Ed8LRwY5A/92v3F+zRnVCgvZxJHg/O93XoVKp3mp9arWaXTvXERK0j0MHNlO8eFEAvh/en4sXggj0302g/24qVPj8tetRKBSsXDGf40cPEui/m88/L5uq3M21K6dCvTlx7BBftWwCQLFiRfDx2k5I0D62b1uNubkagEEDexF60ovQE576ZV9q27YFWzaveKvnljbbsaMHCXhNtuPHDtHSIJu313aCg/axzSDb4p9mcvqUDwH+uwnw3421tRVFihTm4YPf9PcNHeKWqWwrVszj6JED+B9+NZura1dCT3px7OhBWrZ00Wfz8tpGUOBetm1dpc82etR3hJ3xIzBgj37ZIkUK8+B/l/A/vBv/w7sZkolsb5N9+qLxuHtvYPP+NZT+7NNXlilSrDC+p/ZipjLLse1m1aUr1+k9ZOw7255CoWDx0ln4B+7G02cb5cqVSVXeq/e3hBz1ICBoD81bOAFQokRxDnpuweewO79sXqbft/b2Nvgcdsf38E42b12BKhv1qVAoWLpsNoHBe/Hx3fFKrt59OnP0+AGCQvbR4kvn5Fwli+PptZXD/rvYvGWFPheAubka/8DdVKhQLlMZcqq9SG/Zl4YN7cvcORMAKFnyA317Fui/m0f/XKV/vx5vlTO7bUf16lX07UOA/26iIm/RrFnjbLUdhhkXLZ6Bt787Hp6b+axc6VTl3Xt1xD9kLz4BO2navDEAhYsU4vrtU3h4bsbDczP9B/YEYMB3vfAN3IVv4C5Gjxuc6SzpZVuxfC5HQjw4fHgXn5crm6rc1bULJ094cfTIAVp+6ZKqbOgQN2bPGv/KOletnJ/u/dnJuOCnaXge3sE+z18p+1nq+uvWsyN+wbvx8nfX11/BguYsXzMfD+8teAe4Y2dvk6N5Fi6ejpe/O/s8N1M2nf3pF7IH7wD3VPvz6u1Q9nluZp/nZvoN7EEVm4r62/s8N3Pv74s4udTPdrb5P03loN82dh/a9Epdde3ZAe+gnRw6vJ0mzRsBYF7QnKWr57LPezOH/Hdga2/DByWKs/vQJv3f1buh9OjTKVvZhNz3Xoz4/9eMHjWIbt2+4XlM7Ctla9Ys4tvO/bl16y6ufbpQpsyn3Lhx643rHDigJ5cvX2fmrJ/o1KkNEycMZ+SoadjZVaVPn+GcO//bW2Vr27YFarWK+g3bUMvRnkULp9L+G1cg+Q13yBBXatVuiVqt4kjIfvwDjjJ50gh2uHuwecsuxo4ZTP9+Pdi6bQ8DB/aiRs1mqNUqfrsYgpd3AAA//TiDZs0ac/HilUzUWkq2BrpsCxdO5ZvXZAvQZXPXZRujy7Z02Xrs7Gxo+VVXHj9+ql+/s3MDdu704PsRUzKVC6BtmxaoVSoaNmqLo6M9CxdM4ZsObinZBrtSu05ytpDg/QQEHGOSLtuWLbsZM3ow/fp1JyjwOJ07f029+q0BOHrEg+DgE9jZVWXnrgOMyEK2N2nSsjEqlYrOLd2oXqMq46Z/z+Beo/Xl9Z1qM3LyEIp/UDTHt51ZG7ft5pBvEObqt/tAnBNatW6KSqWiqUtHajrYMnvuBLp2Hggkd/AHDOpF4wZfo1ab4eu/i+CgE4wYNZDt2/bhvmM/4ycOo49rF1at3MTSFXPp1X0It2/fo2evTpQq/Qk3f7+TpVyt2zRDpVLh4vQNDg62zJ0/ic6d+ifnKlmcQYN60aB+W9RqFf4BuwgKPM6oUYPYtm0fO7bvY+Kk4bi6dWXlio3Y2duwdNlsPvnko0xlyKn2wn2nR7rLKpVK1q5ZiKODPfs9vAD4++9/cWnaEYDatWowa+Y4ft6w7a1y5kTb0US37W++acWDh39z+HBIttqOl1q2aoJaZUbLpp2pUbM6M2aPp2fX74Dk46zfgB40bfwNKrUKT9/tHAk+QbXqldm/x5MJY2fr11Om7Kd06NSG5s4dSUpKwtN3O96eAVy9Imc5W5s2zVGp1TRq/DWOjnYsWDCFDh1T2rfB37lSp+5XqNUqgoP2ERB4DKVSyerVC3B0sGP/fu9U6+vbtxtVqlTk2LFTWc6U1petmqBWq2jVrAv2Naszfc5YencdAsAHJYrTd0B3mjt1QKVWcdBnG0eCT/DdMFeuX/2doQPHU6lKBapUrcj5c2/3Xvk2eVQqFV/p9+c4enUdbJCnB810+/OQwf702OPFRIP9CdC+VfIHutZfN+fvv/4hOPB4trK1+MoFlUpFm+bdsK9Zjamzx+Dabag+m+uAbrR06oRKrWK/zxaOBp9k0NA+XL92k+GDJlKpSgUqV5W4cO43OrbuA0ANh+qMmzycbb/uyVa2PCHm+OccSZIuSJI03uD2SkmSYiRJMtXd/lCSpCRJkspJkjRakqR7kiRFS5J0XJKk2mnW9dpyg+XUkiQdkSQpVJIkS919PSRJuqnb9nrALM1jRkiSdEOSpDhJkp5IkrRFkiRLSZJUuttuaZYPkiRpRlbr5dbte3Ts1O+V+ytU+Jwnj58ybGhfggL2ULRoYW7cuIWpqSnr1v5AcOBejgTvp1HDOq88tl49B/z8QgDw9Q3GxaUBAPb21Rg3bihHgvczbuyQN2arX9cRv8PBAJw+c44a9tX0ZQ4Otpw8GU5cXByRkVHcunWXajaVqFcv5TG+fsG4ODfg8eOn2NdoSkJCAh9+WIJnERH69YSeOsuQIRPevsJePsdMZLuZTjY/v2CcnRugUCj44ovPWL16IUdCPOjd61tdXdlgZ2dDYMAeduxYy4cflnjrbHXrOXD4cAgAZ86cw96+eupsoWGp6s3GphL16qY8xs8vCGfnBlSs+AVHjoai0WjQaDTcvHkHG5vK2NtVw862KgH+e9ixfU2msr1JjVrVORZ0EoCLZy9T1bZSqnKtVotrh8FEPI3MsW1mVamPP2LJ3MnvdJu169QkMOAoAOFhF1KNCtaoWZ3Tp87q9m00t2/fo2pViQnjZrPT3QOFQsGnn3zEP/9n77zDqjjaPnwfQA7KOcSWaHrEMsaKCChiL6jjO55PAAAgAElEQVTRxMTEmqjYe4uxG3s39hZrjN3YFaUXYwEEsZe1YEl980WlqvTvjz0cDigqTXhf574uLt19dnd+55nd2dlnntn9518qVCzHwweRDBzsxhHP7ZQoUTzHnX4AZ2cHfH2OARAaeg57E10ODnYEG3XFqLqqV2bsmBns3LEfjUbDu++pugC0lpZ06TSA68qLgwym5FV7kdW2VlZatm7dw5y5y55Z/pIlMxg8dDwpKc+/c+dV25FGsWJFmTx5lPFBPDdtRxp16tbGz+84AGfCzmNXq5rRVqt2DU6HnCUhIZGY6FhuR9yjSrXK1LSrRo2aVTl4ZAsbfl5KmTJv8sfvf9OpfR9SUlJITU3FoogFT57EZ1uPKS71nEzat7PYm/rPwY6gZ7RvVlZatm3by9x5yzP+zjr2ODnZv/BhLbs41bXH31ftEIeHnaemnan/qnM6JDzdf7fvUaWqoHHT+iQkJrJj7zq+HT2QwFx2qE2pU7c2ASb1WdOkPu0z64m4S5Vqghp2Valeswr7j2xh3c9LeKvMm8Z9ihUryujxQ5mY6aEgJzjVtTc+PISHXaCGXVWjza52dcJMzrU7Eff4uKqgcVMXEhMS2bZnLSO+G0Cg38kMx5wxbwLjR01/4bUoKXjyO9XHA2hqstwEKArYG5ZdgWtAC2A4MAioBRwF/IUQ5QCEEP2fZ0/D8EDxC6ADWiuKEiuEaAJsBBYZyo0FPjXZpwswFfgWqAi4Ae2A/oqixAN7gC4m278DNAK259Qp+/cfJTEx8an1pUuVwNm5Nj+u+RnXVp1p2qQ+TZvUp3evrty//4Amzb7kiy97sWzZrKf2tdHriYpWO2YxMbG8YWMDwC+/HGTQ4LE0d+2ISz2np4bQM6O30REdFWNcTk5OwdzcXC3DRk90dHrnLyYmjjfesMFGryPKsE9MTCxvvKE37JvMoIFunDx+mH37jhj32737EKmp2c+ps7FJL+dZ2qJMtMXGxGHzhg36Z2izti7GylU/0aPHUNq0/ZoBA3pQvfrHKMpNpk1fSLPmX3HokCdLlrx8A6v631Rbcro2vT6DT9N06G306dpi43jDRs+ly9doUL8OOp01JUsWp25dB6yti6Jcv8n0GQtp3uIrDh3yYsniGdn2X1ZY66yJiY4z0Z7uV4BTx04T+TDqWbu+clo0qY+FxasdqDQ9vyGjf/T6jNdLbEwcNjbq+W9ubk5wqAcNGtYlJPgMpUqVxKmOPevXbaNd2+40auxMo8b1cqxLb5P1OafPpDkmk67QMC8aNnQmOOgMAMHBZ/jjj79yoCFv2ousto2MjMLH8NCVmbZtW3DlyvWXGhHNq7YjjV49u7B3r7txxDA3bUcaehsdMVGxJhoz1me0SV3HxsZhY6PjxvUI5s1ZRrs23Tjq7sucBZNISkriwQNV19SZY7h44QoRt+5kW09mbdFR6T5KTjHRlsm3MbGqryIjo/DNVHdly77F95O+ZfjwibnS80yNeh0xz7keYqLTfRsXG4feRk/JUiUoXtyGLl/2xdszkCkz8y6FUG9jnenaSNeje2Z96rl5PYIFc5bzRZtueLr7MntBepCja7evOHzAiwcPInOtTae3zuCrlBTTNs2a6Ey+srHRUbJUCd4obsPXX/XDxzOQyTPSR4VbtG7C9Wu3uHXzTq61FQSpKan5+lfYeBUdfxchhKUQogzwEeAHpIVOWqJ24icA4xRFOaIoyg1FUWYDJ4C05MQX2QE0wAbAFnBVFCXt6hgAHFAUZZWiKApqB/+ayX5/Am6KorgrinJXUZRDwDGgisG+FWhs0A/QGQg3HCtPuf/gITdv3eHq1RskJSXh5R2IvX11qlWrTKtWTfHz2c3uXWuxsLAwzhHw89lNT7fORMfEoNfpALWRS4uwL122nvv3H5KYmMhRDz/sTKIgzyImOhadXmdcNjMzIzk5GYDo6Bh0unSbXm9NZGQU0TGx6PXW6WVHpt8gVq3exHsf1KJB/bo0bpTzTo5afiz652jTm2jT6a2Jiowi5hnaHj16zPLl63n8+AmxsXEEBJ6kRo0qBAScJDBQjWIcOOCBXc2qvCyq/62frS0mBp0+3ZamIyY6Jl2bzprIqGiuXbvJqtWbOHxoC/PnTSY09Cz3/31g0KZG5Q8c9KCm3ctrexFxsXFY64qZaNcYtUvIcH5DRv/ExMRmqFud3trYCUpKSqKOQyuGD53Ij+t+4MGDh0RE3EW5dpOkpCR8fX594fX4PGIynfOm51xMJs16vTVRho5bUlISDrVdGTpkAuvWL8xx+aqGvGkvstr2eXzd9UvWr9/6Ujrzqu1Io0uXL9i4cYdxOTdtRxqqL5/dhsTExKIzaV90OvU8O/5rMCd+DQHgqLsP1Wqoty2t1pIf1/+ATmfNmG9zPDidSZuJ/zQm2jL5Vq/L6CtTvmzfhlKlSnDw4GZGfzeITp0/p1u3DrnWB0/76Hn+s9ZZEx0VzcMHkXgd9QfA2yMgT9vVmOi4LOszNsv6DDGpT1+q10gfff2yY1u2bX56XmBOiI2Jy+grjWmbFveUr6KiYnj4IBJvD3UEzMczkBombdeXHdqy9ee80SbJf/K7438KSATqokb7QwB/oIEQQoMa6T8GfACsM6TxxAohYg3bC0O6TpZ2k7LaAN2AaOCByfpqQHjagqIoqUCoyfIx4DchxEwhxB4hxFXDsdJCnseB34C01qkrkLdjlAYiIu6hs7Y2TjyrX9+JK1euoyg32bXrIM1adKDNp93Yu9ed0LBzNGvRgWYtOvDTpp2cOhVGa8MEvlatmnDixGlsbPScP+uPtbXaqWvSxIXw8AvP1XAyKJTWrdTj1HGy59Klq0ZbaOg56td3QqvVYmOjp3Llily6rHDqVCitW6kTulq1bMKJkyFUqlSe3b+sAyAxMZH4+PhcDwGeyqW2lkZtthwLPICZmRkWFha41HPi7NmLrFnzA+3btwHUfP+XnRcBEHQqjFYGbU5O9ly6nP5sGRp6jvoudUy0VeDyZYVTQen7tGzZlJMnTlO6dElKlypJk6bt+XbUFN577x0uXVZY8+MC2n/xiUFb/TzLQwUIP32eRs1dAKhZuxrXr2Yv3eN/nZDgM7RwbQyAg6MdVy5fN9rOhJ3HuZ4jWq0lNjY6hCjPlSsKCxdPo0FDNRsxJjaOlJRU7tz+DZ11MeMk3Houjly9ev2p8l6W4KAzuBomBTo62nHZJIc7LOwc9Yy69AhRgSuXFRYvmU5Dg67Y2LhcX5N51V5kte3zsK9VnVNBYS+lM6/aDlBHCLRaLb///qfxGLlpO9I4HRJO8xYNATWF7OqV9HPj7JkL1HWujVZrid5GRyVRnmtXrrNk+Uw+bdcSgAaNnLlwTp03tWXHKi5fUvhuxJQ8Sb04FRRq0r7V4rJp+xZ2DhcXp6fat2exctVPONdrg6trRxb8sIpdhjlOeUFoSDjNXFX/2TvU5FoG/12kjon/Klay5drVG5wOPkMzV3Xyal0XB5RrN/NEC6j12ayFeuzM9Rl+5iJ1nR3S9Rjqc/HyGbRt5wqo9XneUJ96Gx2Wlpb8+cffeaItNOQsTVuk+aoGV6/eMNrOnbmIk7N9Bl8pV29wOjicZoZ96tSrzXUTX1W3q0JYyNk80VYgpOTzXyEjX8fMFUVJEkL4oqb7lEXt5B8DRqGm3VgZlgF6AJnPnMekd8Czsqfxf6gpOV5Af+BHE5sm034JGH67EMINWA38hDpCMQMwhkgURUkVQmwHOgkhvAA7oO3zf3n26Nz5c3TW1qzfsI1+/b9j6+YVoNEQFBTGUQ8/LC0tWfPjAvx996C30fPjjz8/lSrz45qf+WnjUo4F7CchIZFvug8mOjqGSZPn4uuzm4T4BPwDTuDh6f9cLQcOeNC8WUOOHzuIRqOhd9+RjBjej5u3buPu7sOKFRsJDNiHmZkZ30+eR3x8PLPnLOWnDUvo07sr/95/wDfdBvPo0WMuXLjCyeOHSU1NxdPLn19zOZErTduvBm19nqNtsom2jRuW0NugrZtB2/Yd+zh54jCJiUls3bqHK1euM3HibNatXcSA/t2Ji3tM/wHfvVhUmraDHjRr3oBjgWped99+3zJ8eF9u3bqjalu5kQD/vQZt84mPj2fOnKVs2LDEmMrVrfsQHj16TDnbDzh10p2EhETGjZ9JSkoKEyfNYe2ahfTv34O4R48YMGB0rnxpis+RQOo1qsOOIxvQaGD8sOm4DejK3du/E+D17DSL14nDh7xp0rQ+3r670Whg0MCxDB7Si4iIu3gc9WPN6p/x9N6FmZkZM6YtJD4+gR9X/8zipTMYM24oqSkpjBo5mcTERIYMHs/6jYvRaDSEhITjbZiXkxMOHfKiabP6+PrvQaPRMLD/aIYM7U1ExF2OHvFl9eqf8fb9BTMzM6ZN/YH4+ARWr9rE0mWzGDd+GCkpKYwckbvJ4nnZXjxr26woXbokMbFxWdqz0pnbtgOgUkVb7tz9LcPxc9N2pHHksA+NmrhwxHsHGo2GYYMmMGCwG7cj7uHl4c+6NVs47LkdMzMNs2csJj4+gRlTF7J0xWx69u7Co0ePGTl0Ep+0bY6zixOWlpY0a64Ors+ctoiw0HPZ1pTGwYOeNG/WgMAAdX5Iv36jGD7M0L4d8WHlqo34++3FzEzD5Cnzn1t3+cXRw740bFyPw17b0Wg0jBg8gf6De3A74h7eHgGsX7OVgx5b0ZiZMXfmEuLjE1i6cC0Ll8/A3XsHiUlJDBswNg/1+NCoST3cDfU5fNB4+g92407EXbw8Ali/ZgsHPbdhZmbGnBmqnplTF7JkxWzcenfl0aNHfDtUvT7LV/iI3+79kWfaPNx9adjEmYNeW9GgYeSQSfQb1IPbt+/h4xHAxjXb2Hd0C2ZmGubNXEZ8fALLF61jwbJpHPLaRmJSEsMHqPP0SpYqQVzsozzTJsl/NDnJtc4OhomxbkBxYBhqik4kcBDQKorypRDiL2CeoihLTPZbAlxQFGXjS9g3YXiPvxBiAjAa+FhRlL+FEDuAYoqitDPZN9ywr5sQIhTwUBRlssGmAS4BYYqi9DCsq2xYNw9wVBTFNTs+sLB8t/AleRVyMj+pFSY0msKpzvaN7L2R5VVy8cqugpbwXEp/1KKgJTxFciGeJPckKaGgJWRJ4bw6VYoX1b14owIgOr7wdtxKWBVOnwE5mqv2KrAwM3/xRgXEHw8vF7pL9P6njfK1IksdPlaofvOrmCXngRpRTwaCFUVJFEKcQs2VT3u1zXxgsqGDH4qaTjMEdRLty9hN+QF1dGCJoYylwHEhxLeAO9AdqAmk5bzcB5oIIaqg3jNGoub3G8dqFUW5JoQ4hzo/YECuvCGRSCQSiUQikRQA+f4BL0VR/gSuokbQ01JzjqF2sj0My0tRO+zzgStAR+ArRVFOvqTdtLwE1IeCTkKIVoqiBKPm5/cFzqN26neY7DIcSAXCAF9AC8wh/c1DaaS9xWdfNl0gkUgkEolEIimMvGY5/vme6vO/ghBiAfCeoihdXrhxJmSqT/YpVONimZCpPtlHpvpkH5nqkzMK59WpIlN9so9M9ck+MtUne/zbOn9TfUp7vH6pPv/VCCGcgOqoE4bbFLAciUQikUgkEokkR8iO/4tpCnwPLFcU5XhBi5FIJBKJRCKR5BGFd3A1X5Ad/xegKMpcYG5B65BIJBKJRCKRSHKD7PhLJBKJRCKRSF5LUmXEXyKRSCQSiUQikbxKhBAWwELU19ZbAFuA7wxvrHzW9iOBEUAp4DgwQFGUu88rI99f5ymRSCQSiUQikRRGUlPy9y+bzAZaA58Bnxv+nf2sDYUQA4AZqN+YckJ9UNj9ogJkx18ikUgkEolEIilAhBBWwCBglKIoQYqiHAOGAgOEEEUzbasBJgGTFEXZqyjKFWAgUFYI8e7zypGpPhKJRCKRSCSS15JClONvB1gDv5qsO2ZYZwcEmayvAryLSYRfUZSbwAcvKkRG/CUSiUQikUgkkoLlXSBOUZSotBWKokQDj4D3Mm1bAYgHbIUQQUKIv4QQe4UQ77yoEBnxfwUUqk+2mWBmVnif+wrzV0sppF9rrFfsw4KW8F/Lv3d8ClrCM3m/QuH8ZmDRIpYFLeG/kkeJ8QUt4ZloCu1dCryLVyhoCc9lG4Xvy8I/R54raAn/XaQWmvO/GGpnPjPxgDbTOj1q8H4dMBr4B5gFeAoh7BVFScqqENnxl0gk+U7pj1oUtIQsKaydfolEIpG8Vjzm6Q4+hnWPMq1LBIoAYxVFOQwghOgC/A00BnyzKqTwhnwlEolEIpFIJJJ8pBC91ed3wFoIoU9bIYSwQR0J+CPTtn8a/r2UtkJRlP8D/gWeO/wvO/4SiUQikUgkEknBch6IAxqYrGtkWHc+07ZnUUcIaqetEEK8BZQGIp5XiEz1kUgkEolEIpG8lqSmFI4cf0VRHgsh1gErhBA9UKeILgdWKoryRAjxBmCpKMr/KYoSK4RYBSwSQjxEHQFYDFwm41uBnkJ2/CUSiUQikUgkkoJnHFAUOAwkAduAiQbbUtT8/Y8My2NRJ/5uQZ3s6we0VhQl+XkFyI6/RCKRSCQSieS1pBC9xx9FUeKBAYa/zDa3TMvJqA8FEzNv+zxkjr9EIpFIJBKJRPIaICP+EolEIpFIJJLXktTC8x7/V4KM+EskEolEIpFIJK8BMuIvkUgkEolEInktKUw5/q+CAov4CyHuCCGGZGHbJITYk49l7xFCbMqv40skEolEIpFICj+pKZp8/StsFNZUn+FAn4IWkZ9oNBpWrpjL8V8P4euzm/LlP8pg792rK8FBRzlx/DCffNIcgFKlSnD0yHYC/Pexbdtqiha1ynC8w4e20K9vNwCKFSvK3r0bCfDfh/vhrZQuXTJHGlcsn82xwAN4e/9CeduMGnv16sKpk0f49dhBPmndLINt6JDezJwxzrjcsWM7jv96iMCA/axYPhuNJncXQ5r/Tvx6CL/n+O/k8cO0MfGfx5HtBPrvY7uJ/4YP68upE4c5deIw308amSs9eVGfixdNJyTYA1+f3fj67MbGRs9HH72Pv99eAvz38fOmZRnqPqd6u83qx4R9sxizcxpvfVg2g71F77ZMOjCHSQfm8NnwDsb1C4PXMGbnNMbsnMaXY7rmSkNmPYuXzsDHbzfuHtuwtc344cEebp0I/PUAvv57aNmqCQBvvVWaQ+5b8PDeyabN6T6xt6+Oh/dOPL13sXnrCrRayzzT+SIuXL6G25Axr6QsjUbDvEVTcPfewT73n/mo3AcZ7F9374BXwG6O+OykRcvGgNouLP9xLgeObuGo705q2Vc3bl+0qBWHPLdRoWK5PNG2YPE0jvrs5ID7ZsrZZtT2TY8O+ATuxcN3l1Fb8RJvcC0imAPumzngvpl+A7oD0KtPV7wD9uDlv9u4bWHR1n9QDzz9fsHT7xe+Gzs417qWLpuFf8A+PDx3PnUNuPXszPEThwgI3E+r1k0BeO+9d3B334qH5048vXZRsaJthn2Wr5jN9Oljc6UrTdvy5bMJDNyPt/eup7T16tWFkyfdOXbsAK0z3QuGDOnNDJN7Qdeu7QkN9cLPbw9ubp1yrc1EJO/OGkT5fQuw3Tkbyw/fzmB+Z0o/KhxejO3O2djunI2ZvhhF3nmTcltnqOt2zUFr+27e6clSpoYvZvVm0L5p9Nv5PaU+LJPB7tytBUMOzmTIgRlUblor3zQU1rZDkv8UylQfRVGiClpDftOuXSusrLQ0aPgZdZzsmT9/Ml9+2QuAMmXeZMiQXtSp+wlWVlqOBe7H1/dXJk0cyc6dB9i85RdGjx5Mv77dWLpsHQDTp4+lZMnixuP36f014eEXmDVrCd27dWTC+OF8O2pKtjR+9llLtFZWNGr8OU5OtZg373u+6tDbqHHwoF4412uDlZWWAP99+Podx8zMjNWr5+HkWIv9+48CYGVlxbSpo7Gv3ZzHj5+wefMK2nzSHPcjPrn2X32D/xbMn0z75/jPx+C/HQb/jTH479BhL7p0+YJ6Lm1JTU3lWMB+Dhz05OLFqznSkxf1WatWdT5p05X79x8aj7927ULWrtvCzp0H6NWzCyNG9GfOnKU59l8tVyeKaIswu/1EbGtVpNOkHizvOw+AN99/C+d2DZjx+XhITWXc7hmEe50m4XE8dy/dZlmfuTkuNyvaftoCrVZLi2YdcHC0Y+bs8XTtrL7N7K23StN/YA8aN/gcKytLPH1+IcD/JCNHDWD7tn3s3LGfcROG0bNXF1at/ImlK2bT45shRETcpXuPjrz/wbvcvHE7zzVnZuO23Rz29KeolTbfywJo3bY5VlZa2rp2wd6hJlNnjcGtqzqI+uZbpenT/xtaNvkKrZWWQx7bOBZwkkHDenHtyg2GDhjHx1UrUbVaZc6GX6SmXVXmL57K2++UeUGpL8cnbZtjpbXkkxadqe1Qk2kzx9G96yBArc++/bvRovGXaK20uHtu51jASWrUrML+Pe6MHzPTeJySJUvQs09XmtT/HK2VlpMhR7Cr2rhQaPvwo/f4quNntGzagdTUVNw9t3PU3Zcrl5Uc6fr0M1estFqaNmmPo2Mt5sydRKeOfQG1DRk00I369T/DykqLr+9u/P1OMHnyKH5csxn3w940b96QadPH0LWLet306t2VqlUrc+J4SK78Beq9wMpKS+PGXxjvBR069EnXNqgn9eq1xcpKi7//Xvz8jmNmpmH16nk4OtZi/34PQA12TJ36HXXqtCYyMhoPj+0EBJzk7t3fc63RxrUuGq0lt9qPplgtwduTenG37yyjvWi18tzuPoXkh9HGde9M7cf9zUeI9g5G17AWZcd05+6AObnW8jyquDpgoS3CqvZT+KBWBdpM+obNfRcCUKyEHuduriz5ZBwW2iKM8vmBOfWemRiRKwpz21EQpKYWtIJXy3Mj/kKIc0KIcSbLK4UQcUIIC8NyWSFEqhCivBBimBDiuhDiiWG/T0z2CxRCrBJCXBVC/COE+DhTObZCiL+EECsMy8ZUHyGEmxAiTAgxxrDNv0KIrUIIa5P9OwohFCHEYyGEuxBimWkqjxCimxDipkH7OsAyU/kjDdoThBAPhBBbhBA6IYTWsNw70/b+QohpL+/mp3Gp54SXdwAAIafDqW1fw2hzdLTj1KkwEhISiI6O4eatO9So/jEuLun7eHkF0LSp+lXn9u3bkJKSgqdXgPEYy5avZ86cZQC8/8E7/Oeff3Ok0ds7EIDTp89ib6rRwY6goFCjxlu37lC9+sdYWWnZtm0vc+ctN24bHx9Po8af8/jxEwAszM15Eh+fbT2m1M+G/249w3+eXgE0a9qA3377kzZtvyYlJYXU1FSKFLHgyZPsa8ur+tRoNFSoUI7Vq+dzLPAAbj3UiNjHH1fE01Pd9lRQKC71HHPgtXQqOlbm0rFzAEScvcFH1dMjhQ/+us+iHjNJNfjE3MKcxPgEPqxuS4myJRm9YyojfppAWdt3cqXBlLrODvj5qh8bDAs9lyGaVNuhJiHBZwz+iyUi4i7VqgnGj53Jrp0H0Gg0vPfu2/zzz79UqFiOhw8iGTjYjSOe2ylRovgr6fQDvP/O2yyZPemVlAXgVNcef98TAISHnaemXTWjrVbt6pwOCSchIZGY6Fhu375HlaqCxk3rk5CYyI696/h29EAC/dT9LbWW9PxmaJ75qk7d2vj5HQfgTNh57GqZaqvB6ZCz6doi7lGlWmVq2lWjRs2qHDyyhQ0/L6VMmTd58OAhjV3akZSURJm3ShMVFZ1Vka9c2x+//02n9n2MbYdFDtuONOo5O+LjcwyA0NCz2Ge6BoKM10AMtyLuUq16ZcaPn4mnhz8A5hbmxBvKd3Kyx8mpFhs3bM+xngza6jlmeS9wcLAjKCgs072gMlZWVmzbtpd5JveCcuU+4Pz5Kzx8GEVqaiphYRdwcsqbqLa1YxVijp0B4NFZhWLVK6YbNRosP3qb9+YMpvyeeZTooI66/jVzI9H+oeom5uakxCfmiZbnUc5RcP3YeQDunb3JeyZt76OHMSxpPZaUpGT0bxbncXRcvmgozG2HJP95UaqPB9DUZLkJ6hfF7A3LrsA1oDMwDZgM1AAOAIeEEDVN9u0DjADaKopiDKcKIcoCPsARYGgWOmoADYDmQF+gPYaPGwgh6qF+2WwVYAdcAIyPyEKIJsBGYJFBdyzwqYm9CzAV+BaoCLgB7YD+hg8p7AG6mGz/DtAIyFWLamOjIyoqxricnJyCubm5waYnKjr9BhcbE4fNGzbo9en7xMTE8sYbeqpWFXTu/DlTpy54qoyUlBS8vX5h8KBeeHr6Z1uj3kZHtMmNNjkl2ahRn0l/TKyqJzIyCl/fjF+LTk1N5R/Dg8eggW7odNZPbZMzbVn7L9rEfzExcbzxhg02z/BfUlKSMbI+f+73nD13mRs3IrKtJ6/q09q6GCtX/USPHkNp0/ZrBgzoQfXqH3P+/GU+/dQVgLZtXbG2LpZtjaYU1RXlccwj43JKcgpm5mpzkJyUTOxDVVfHCd25d+U2/7n9F1H/POTIqv0s6DKVIyv30XfxsFxpMMW0biCj//T6jHUdGxOHjY0eAHNzc4JDPWjQsC4hwWcoVaokTnXsWb9uG+3adqdRY2caNa6XZzqfR4sm9bGweHWDqHq9jphoU58lZ/BZTHSs0RYXG4feRk/JUiUoXtyGLl/2xdszkCkz1bSk0JCz/PnH33mnzUZHTFR6+Zm1RZvojo2Nw8ZGx43rEcybs4x2bbpx1N2XOQsmGfft3fdrPPx2cfigV6HRlpSUxIMHatsxdeYYLl64QsStO7nSFZ1Ffdo8dQ3E8oaNnvv3H5KUlETFirbMnj2R2bOXUrbsm0ycOIKRI77PsZbMZG7fUkzuBZltsbFqe6veC45nOM7Nm3eoUqUSb71VmqJFrWjSxCXXbVka5rpipJi0aanJKWBo08yKWXH/Z3fujVjE7R5TKd9prgIAACAASURBVNXtE6wqf6RG/5OS0dq+y9sTe/HP0h15ouV5aHVFeZJJZ1rbC2pb7NzdlcH7p3PJ43S+aCjMbUdBIHP8M+IBuAghLIUQZVA/E+yH2gkHaAkcRe3Qz1IUZaeiKNcVRZmK2pk3TXb1VxTFS1EU0zO5OOANBAP9FEXJasClCNBXUZTLiqLsBzyB2gbbEOCwoihLFZUJgGkZA4ADiqKsUhRFQe3gXzOx/wm4KYririjKXUVRDgHHgCoG+1agseH3g/qQE244Vo6Jjo5Fr9cZl83MzEhOTjbYYtDr0m06vTVRkVHExMSi16sDHXq9jsjIaL755ivefacsPt6/0L1bR4aP6Iera2Pjvq4tO9Kk6Rfs2rk22xpjomPRmWrUpGuMyaRfr1P1ZIVGo2HunEk0a9aQTp37ZVvLC7Vl8p/OxH96vTWRkVFEP8N/AFqtli2bV6DT6xgydHyO9ORVfT569Jjly9fz+PETYmPjCAg8SY0aVRgzZjpt27bA/fBWUlJS+Pf+gxzpTONx7GOsrE3miJiZkZKc/moDC20R+i0djpXOii2T1gNw58Itzvqo0bEbYdcoUTb780aywrRuAMzMNOnnWkwsOhObTm9t7GgkJSVRx6EVw4dO5Md1P/DgwUMiIu6iXLtJUlISvj6/YmcSzfpfIiYmFp3O1GdmGX1mYrPWWRMdFc3DB5F4HVWDAN4eAdS0q5o/2qIz1tnztOl0an0e/zWYE7+qaSlH3X2oVqOKcZsN67ZRrVIDnF0ccWlQp9Bo02ot+XH9D+h01oz5NleDwKquLOoz+qlrQEekISjTsKEzO3etpU+fkdy4EcEXX7ShVOkS7Nu/iW9HDaRjp8/45puvcqUtc/um0Zi2bxmvXZ3OOst7QWRkFKNHT2fnzjWsWfMD585d4t9/c9eWpZEc+wgz66LpK8w0YGjTUh7H8+9Ph0l9Ek9K3GNiT13A6mM1H93auTofrp3IbyMXER/xR55oeR7xsY/RmujUmGkytL0AQZu9meU0kHJOlbF1rpL5ELmmMLcdkvznRR3/U0AiUBc12h8C+AMNhBAaoAVqJ7s0EJRp3xOA6Zlx6xnHnwRUA35TFOV5L1SKURTF9JEyGvVhANTRgNBM25tqqQaEpy0YHi5CTZaPAb8JIWYa3vZzFWgDmBs2OQ78BqTNcOyKOsKQK04FhdK6lTqYUsfJnkuX0nPKQ0PPUb++E1qtFhsbPZUrV+TSZYVTp0Jp3UqdONWyZRNOnAxh/PhZuNT/lOYtOrB5yy8sXbIWb+9AxowZwtdffwlAXNxjklOy/76qU0GhtDJodHKqxeXL6c9LoWHncHEx1ViBy8/JbV25ci5WVlq+6tDbmPKTG07m0n+tDP4D2L93IxcuXGHQ4LGk5MBPkHf1WamSLccCD2BmZoaFhQUu9Zw4e/YizZs3ZObMxbT99BtSUlLxyxRJyy43w65RvYk6cGdbqyJ/KPcy2IetG8tvV++yecJaUg0++WxER1r0agPA+x9/yP0/s58+lhUhwWdoYXhgdXC048rl60bbmbDzONdzRKu1xMZGhxDluXJFYeHiaTRoWBeAmNg4UlJSuXP7N3TWxYyTD+u5OHL16vWnyvtfIDQknGauDQGwd6jJtSvpv/PsmYvUca6NVmuJ3kZHxUq2XLt6g9PBZ2jm2giAui4OKNdu5ou20yHhNG+haqvtUJOrGbRdoK6JtkqiPNeuXGfJ8pl82q4lAA0aOXPh3GXKVyjHT1vVVJHExEQS4hOM52NBawPYsmMVly8pfDdiSo7bjjSCgsJo2VKduO7oWCtDe3om7Dwu9RyNbYgQFbhy+ToNGzqzYMFkPm/Xg7PhFwFYvXoT9V0+pXWrzixauJpfdh1i69bcvSQvKCiMVoZJ9ZnvBWHZuBeYm5tTp04tmjX7it69RyJEeYKCwnKlLY24sKvomzgAUKyW4Ily12jTlnuH8nvmgZkZWJhj7ViFx5duYe1cnXcm9+V2jyk8vpg/10Jm7oRdRzSxA+CDWhX4W/nNaCtt+zbdflRfMJGcmExSQiKpKXmfgF6Y246C4HWL+D93XFpRlCQhhC9quk9Z1Ej4MWAUatqMFWr0/VloyPhg8fgZ2/wK/AxsEkJsUxTlYhbHSsji+KA+mLzoASaz5xMw/HYhhBuwGvgJdYRjBmraEqA+KAghtgOdhBBeqOlEbV9Q3gs5cMCD5s0a8uuxg2g0Gvr0HcmI4f24ees27u4+rFixkcCAfZiZmTF58jzi4+OZPWcpGzcsoXfvrvx7/wHdumX9FolNm3ayccMSerp1xtzcnL59sv+2moMHPWnerAGBAfvRaDT06zeK4cP6cuvWHdyP+LBy1Ub8/fZiZqZh8pT5xGeRt29nV42ebp05cfI0Xl67AFixYiOHDmV16ryYNP8dN/iv93P8972J/37asIQ+Bv99020w7dq1omHDumi1lrQy3HQnTppLcMiZHOnJbX0+evSY7Tv2cfLEYRITk9i6dQ9XrlxHZ23NurULiY9P4MoVhaHDJubYdwDhXqep0qAmE/bOAg1sHL0S195t+efu35iZmyHqVMHCsgjVG6v5t3vnb+Poqv30XTKMmk3tSU5KYcN3K3KlwZTDh7xp0rQ+3r670Whg0MCxDB7Si4iIu3gc9WPN6p/x9N6FmZkZM6apfvhx9c8sXjqDMeOGkpqSwqiRk0lMTGTI4PGs37gYjUZDSEg43l6BeaazMHH0sC8NG9fjsNd2NBoNIwZPoP/gHtyOuIe3RwDr12zloMdWNGZmzJ25hPj4BJYuXMvC5TNw995BYlISwwbk/o0vz+LIYR8aNXHhiPcONBoNwwZNYMBgN25H3MPLw591a7Zw2HM7ZmYaZs9YTHx8AjOmLmTpitn07N2FR48eM3LoJP7zn//j8sVrePjuIjU1FT+f45w6mTnOUzDaPmnbHGcXJywtLWnWXB0EnzltEWGh53Kk69AhL5o2a4Cf/140Gg0D+o9m6NDe3Iq4y9EjvqxavQkf318wMzNj2tQFxMfHM3/+ZIpYWrJ2nTo59MaNCIYNnZAr/zyLgwc9adasAQEB+wz3gu8YNqwPt27d5cgRH1at+gk/vz2YmZkxZcqCLO8FycnJJCQkEhR0hCdP4lm6dF2GlxjkhmivIPQN7Ci/dz5oNPw+eimle7cj4e5fRPueJvJAIBX2/0BqUhIP9/kTf+MeHyxbhsayCO8vVO+P8RF/8MeElXmiJysue4VSsUF1Bu2dBhrYPXoNDXp/wr93/8NV3zP8dfUeg/dPJzU1FSXwPLdDsveiiZehMLcdkvxHk/qC6cyGia1uqGk5w1Aj+ZHAQUCrKMqXQog/gMWKovxgst9RIFZRlI5CiEAgTFGU70zsd4AfFEVZYehQ6wEXQ0d7E6BTFOUrQ8f8B0VRSpvsa2rfAlgritLexH4CuKkoipsQYgdQTFGUdib2cOCCwR4KeCiKMtlg0wCXDHp7GNZVNqybBzgqiuL6Ms5No4jlu4VyzriZWWF9mys5GqF4VRS+53eVbu84F7SELNn779mClpAl/97J+dul8pv3K7QpaAnPJPl1++JNHvEoMXcvNcgvCnN7e7pszRdvVIBsQ/fijV4xP0fm7OHzVfB35NVCdwu9XbNFvvbRyp33KVS/+WVmonmgRsSTgWBFURKFEKdQc937GraZC0wXQvwOnAE6oeb/N35JHUOBi0B/4MeXVq+yFAgSQgxDHX3oBLgAN0zsx4UQ3wLuQHegJuokYID7QBMhRBXUPt1I1Px+4+iDoijXhBDnUOcHDMimPolEIpFIJBKJpMB5YchXUZQ/gauoEfC0dJ1jqJ1kD8PyCmC+4e8i6ltx2iqK8lKJyIqiXAd+AOYY3vLz0iiKEgb0RO2wXwAcUEcjEgz2YNT8/L7AedROvenU/eFAKhAG+AJaYA7pby5KI+0tPvuyo08ikUgkEolEUjh53XL8X5jqU9gRQtRBTSm6bLLuCBCiKMr0PCxnAfCeoihdXrhxJmSqT/YpzEPPhe8yVpGpPjlDpvpkH5nqkzNkqk/2kak+2Uem+mSPiOqu+dpHs73oXah+c6H8cm82cQLGCCG+Bu6iphg1JeOrRHOMEMIJqI6ahlQ478ISiUQikUgkkmyTmlqo+uX5zv9Cx38V6vcFdgIlUNOSvjIdAcglTYHvgeUvm7okkUgkEolEIpEUNv7rO/6KoiSjvl50VD4dfy7q5GWJRCKRSCQSyf8Qr1vmYuFN8pZIJBKJRCKRSCR5xn99xF8ikUgkEolEIskJKa9Zjr+M+EskEolEIpFIJK8BMuIvkUgkEolEInkted3e6iMj/hKJRCKRSCQSyWuAjPhLJBKJRCKRSF5LCuPXdfMT2fF/BViYF043F+avNhe3si5oCVmSUkj9tuf/wgtaQpYUVp8Vdn67eaSgJWRJ7WpfF7SEZ6IptN/WBs/q2oKWkCVVzv1W0BL+K3lAUkFLeIruxe0KWoKkEFM4e6QSiUTyini/QuH8IHdh7vRLJK+KxVgWtIQssZDZ0v8TvG5xKdnxl0gkEolEIpG8lrxuqT7ycVUikUgkEolEInkNkBF/iUQikUgkEslrifyAl0QikUgkEolEIvmfQ0b8JRKJRCKRSCSvJfIDXhKJRCKRSCQSieR/Dhnxl0gkEolEIpG8lrxur/OUEX+JRCKRSCQSieQ1QEb8JRKJRCKRSCSvJfKtPhKJRCKRSCQSieR/jkLf8RdC6IQQvU2WNwkh9rzC8o3lCSE0Qoi+QgirV1W+RCKRSCQSiSR/SE3V5OtfYaPQd/yBUcDAAix/ONDH8P+GwFryMEVKo9GwbNksAgP34+W1E1vbDzPYe/bszIkThzl2bD+tWzcF4P333+HIkW14ee3E23sXFSvaGrcvWtQKf/+9VKpUPk+0LV8+m8DA/Xh773pKW69eXTh50p1jxw7QunWzDLYhQ3ozY8Y443LXru0JDfXCz28Pbm6d8kTbD4un4eG7i4NHtlDO9oMM9m49OuIbuBdPv19wbdU4g825ngPnrxwzLnfo3I5jpw5x2HM7X3f7Kte6Fi6ZjpffLxw6uvUpXd3dOuJ3bB/e/rtxbdUkoy4XRy5e/fWpYy5eNoPJ077Lla40bYuXzsTXfw9HPLY/VZ893DoRePwgfgF7adVKPdfeKlOaQ0e24um9i02bl1O0qPrMO3hob0JCPTnisZ0jHtupULFcrrUtXTYTv4C9eHjueEqbW8/O/HriIP6B+2jVOl2b+5GtePv8wuYtK4zaQL0OfPx2U6mSLblFo9Ewb9EU3L13sM/9Zz4ql7FOv+7eAa+A3Rzx2UmLlo0BKFasKMt/nMuBo1s46ruTWvbVM2g75Lkt1z7LLhcuX8NtyJhXWiao/ps0bwxb3NeyYd9K3v/ovae2KVGqOIdP/YKl1hIAnd6a5ZsXsHH/Kra4r6VG7Wr5pGs0m93Xsn7fCt7/6N1n6jp0alcGXcs2z2fD/pVsziddBnHYfDeSUj+uoOTyxZi/+04Gs83woZTesIaSyxdTcvliNNbWmL9dlpIrllBq5VKKfz8BtNp8kqZh0dLpePnt5rDHNsplula7u3XC/9f9ePvvoWWmNq6eiyOXrh3PN13dZ/Vj4r7ZjN05jbc+LJvB7tq7LZMOzGHSgTm0G97BuH5R8FrG7pzG2J3T+GrM1/mm7ZtZfRm3bxbf7ZzKm5m0Ne/dhvEHZjP+wGw+Ha7egzRmZnSa0pOxe2Yw8dBcajS1zxdtmXW2n9Wbwfum0X/n95T6sEwGu3O3Fgw9OJMhB2bwcdNa+a5Hkj/8N+T4F+jjkqIoUSaLea7ls89aYmWlpXHjL3ByqsXcuZPo2LEvAGXKvMmgQT1xcfkUKystfn578PM7weTJo/jxx585fNib5s0bMmPGWDp37o+9fXWWLZvNu++WfUGpOdM2b973dOjQJ4O2evXaYmWlxd9/L35+xzEz07B69TwcHWuxf78HAKVKlWDq1O+oU6c1kZHReHhsJyDgJHfv/p5jbZ+0bYHWSkvr5p2o7ViT6bPG0a3LIADeeqs0fQd0o3mj9mittBzx2kGg/0kSEhJ5592yDBraC4si6qlfsmQJJkwaQZMGnxMVGc2+Q5v49VgQv937I0e62nyq6mrZrCMOjnbMmD2ebzoPNOrqN6A7TRu2R2tliYf3ToOuBN59tyyDTXSl0aNnZ6pUFZw8cTrHvkqj7aeuWFlZ0rzpVzg62jFrzgS6dOqvaitTmgGD3GhUvx1WVlq8fH/B3/8E344ayI5te9mxfT/jJwynV++urFyxETu7qvTv+x3nzl3KtS6ATz9zRavV0qzJlzg62jF77kQ6d+xn1DZwYA8aGLT5+P6Cv98JRo0ayLZt+9ixfR8TJqZrq2VfnaXLZvLuu2/nibbWbZtjZaWlrWsX7B1qMnXWGNy6DgHgzbdK06f/N7Rs8hVaKy2HPLZxLOAkg4b14tqVGwwdMI6Pq1aiarXKnA2/SE27qsxfPJW33ynzglLzlo3bdnPY05+iVvnTGXweTVs3RGtlSbe2/ahhX5Xvpg5luNtYo71e4zoMnziIUm+WNK7rPqALIcfD2LpuFx+V/4B5q6fTydUtz3VZWlnSvW0/qttXZdTUYYx4StfADLq6DehMyPEwtq37hQ/Lf8C81dPo7NozT3UBWDWoj8bSkvsDhlCk6sfYDBnEw/GTjPYioiL3vx1NalS0cZ1+3GgeHTzMEx8/irb9BF3nDsT+vDXPtbX5tAVarZaWzTrg4GjHzNnj+brzAEBt4/oP7E6TBl9gZWWJh88uAoxt3NsMHtr7qTYur7B3daKI1pJZ7SdgW6sinSf1YFnfeQC8+X4Z6rZrwIzPx0NqKuN3z+SM12kSHsdz99JtlvaZky+a0rBzdaSI1pK57SdiW6siHSd1Z2Xf+QCUfv8t6rRrwOzPJ0BqKmN2Tyfc6zQfVrPF3MKceV99T/EyJandpm6+agSo6uqAhbYIK9tP4YNaFWg76Rt+7rsQgGIl9Dh3c2XJJ+Mooi3CKJ8fuFpvSL5rehW8bm/1yfEVKIQ4B+xUFGWuYXkl4Aa8oShKkhCiLPAXUB5oDwwFSgHngO8URQk27GcNLAA+B94E/gbWKIoyUwjhBkwxbJcKpIXIigkhNgAdgHjD9sZWUQjRHZgEvAdcA75XFOWIwbbJ8LsrGv46Go6xEKgOPAS2AuMVRUk2bK8DvgMCDEXECCF6KoqyKaf+S6NePUd8fNTo8+nTZ6ldu4bR5uBQk6CgMBISEkhISCAi4g7Vq1dm3LiZREXFAGBhYcGTJ/EAaLVaOnfux4YNi3Mry6jN2zvQqM3e3lSbXQZtt26p2m7dusu2bXvx9z9BpUoVAChX7gPOn7/Cw4fqM1RY2AWcnGrlquNf17k2/r5q5OhM6HnsaqVHVO1r1+B0cDgJCYkkJCRyO+IeVatV5splhYVLpjNy2Pf4/boPgA/Lvc+li1eJNGg7G34RB0e7HHf86zrXxt9HjdqHhZ7DrlZ6RNDeoQYhweEm9XmXqtWEqmvpDEYOnYT/8f3G7R2d7HB0smPTxp1UzIPItXM9B3wN2kJDz2WIQteuXZPgoDPp2m7doVq1yowbMwONRoNGo+Hd997mZuBtAOzsqvHtdwMpU+ZNvLwCWPTD6txpc3bA13AdhIaew95Em4ODHcHBZzL4rVr1yozNpO3GDVWb1tKSLp0GsH7DolxpSsOprj3+vicACA87T0279DqtVbs6p0NMzrXb96hSVdC4aX0O7vdgx951xMbEMv67GQBYai3p+c1QVqyZlyfaXpb333mbJbMnMX76gldaLkAtp5qc9A8G4EL4ZarU/DiDPSUlhX4dh7LTe5Nx3ZY1O0lISATA3MKc+PiEfNF1yj8EgIvhl6las/IzdA1jh/dPxnVb1+wiIUHVYpFPugCK1KhOfIj6sJ94+SpFKldKN2o0mL/3LsXHjMKsREkeHTnK4yMeWHz0IfHzf1D3uXgJq2GD80VbXWcH/HxN2jj79OuhtkNNQjJdq2lt3KKl0xkxdBIBJw7ki66Kjh9z8dhZACLO3uCj6umj3g/++pdFPWaSmpICqOdUYnwCH1W3pUTZkozZMY3EJwnsmPETf0f8mS/aLplo+9BE28O/7rO0xywTbRYkxSdStWFN/rh2j6Ebx6PRwI4pG/NcV2Y+chQox84DcO/sTd6rnn7fefQwhiWtx5KSnIL+vTd5HB2X73ok+UNuUn08gKYmy02AokDaeJQraqe7BWq6zCCgFnAU8BdCpHXiFwHOqB1/ASwDZgghagO7UDvk54G3gd8M+7RGfaiwA8YDE4UQLQEM/y4DJqN25NcAe4QQziZavwZWG/QHAQcAd+BjoDvQF/UhxpTfgC8N/y9v0JZr9HqdsRMPkJycjLm5ucGmJzo63RYTE4eNjZ779x+SlJRExYq2zJkzkVmzlgAQFBTG77//lReyALCxyagtJSVdW2ZbbGwcb7xhQ2RkFL6+GYdyb968Q5UqlXjrrdIULWpFkyYuWFsXy5U2vV6XwTcZ/GaT0RYbG4feRs+8H6awctkG/v7rP0ZbxK07iI8r8uabpSha1IqGjZwpVqxonulKSU4xqc9Mugz1OX/hFFYsXc9fJrrKlHmTseOHMfrbqTnW8iJtySbabJ7hM5s39ACYm5sTEupJg4Z1CQ4KA2DvHndGDJ9E20++xtnZwZgalGNtNnqisqrPTNdI2nWQpi00zIuGDZ0JDjoDQHDwGf74I++uA71eR8xztMVExxptcYZzrWSpEhQvbkOXL/vi7RnIlJlqik1oyFn+/OPvPNP2srRoUh8Li4IZ4NXprYmNSfdRion/AIJ/DSXqYXSGfWKiY4l/Ek+pN0sye8VUls7O3YPls7DWFyPGRFfyS+tKMOiawrJ80AVgZl2M1DiTjlVKCpirt2uNlRWP9u7n4fTZPBg1hmJftMOivC1JN29h5eICgLZ+Pcys8mcqml6vIzrqOW1c1LPauKksX7YhQxuX1xTVFeVxzKMMuswMPktOSib2oaqr04Tu3Ltym//c/ovIfyJxX7WP+V2m4L5yL/0WD88XbVYvqe2rCd2M2nQlbHir3Nss7zUHzx8P4rZgUL5oy6zzSRY605brdXdl8P7pXPTI/Sh0YSElVZOvf4WN3NwJPIBhQghLoATwEeAHNABOAy1RO/kTgHFpEXdgthCiMTAYNYp+EjViH26wLxBCTAaqKIpyRggRCyQpivI3gBAC4KJJhD9CCDEOqAl4GcpboCjKToP9luEhYhSQlsCtpEXrhRAlgZLAX4qi3AHuCCFcgX9Mf6wh+v/AsPiPoiiPc+a2jMTExKLXWxuXzczMSE5ONthi0Ol0Rpteb02UYWi3YUNnli6dSe/eI7hxIyIvpDxFdHQsen16+RpNujbVlq5bp7MmMjL6qWMAREZGMXr0dHbuXMPvv//FuXOX+PffB8/c9mWJiYlFp8vCb9Gx6DJpS0xIoG692pSz/YDR44ZQosQbrPtpMX17juT7cbP5aesK/vrzby6cv8KD+w/zR1cmm05vTUJiInXrOVDO9kPGjB9KiRJvsP6nxZwOOUvJUiXYtXc9b5UpTbGiRblxPYId2/bloTZNxvrUZfRZlKE+k5KScHJoSeMmLqxZt5BPWnVh1cqfjA8KXp4B1KhZBU9P/5xri45Bb3KuZ/ab6blmeh0kJSXhUNuVxk1cWLd+Ia1ads6xhiy1ZaNOrXXWREdF8/BBJF5HVX94ewQwdEQfXldiY+IoloX/nkfFyuWZt2Y6i6Yt50zQ2TzXFRfzCGtdegDiZXVVqGzL/DXTWThtBWeCzuW5LoCUuEdoipkERzRmkKxGhFPj44n7ZS/Ex5MKJJw5S5EK5YlesQqbkcOxatGUhLBwUqKinn3wXBITE4vO9L6Q+Xowsen01iQmJuJczwHb8h8y1tDGbdi0hN5uI/JU1+PYx1hZpwdtNGZmpBh8BmChLULv+YN5EveYzZPWAXDnwk2SDdvcCLtG8bIlyQ+eZNJmZqZ5Spvb/EE8iXvMtknrAYiNjOGCnxrMuB5yhTK2Ged55JdObQYfZtQJcGqzNyE7/Oi1aRzlnatwK+hKvuuS5C25ififAhKBuqjR/hDAH2gghNCgRvqPAR8A64QQsWl/hu2F4ThbgfeEEIuFEEeEEH+gptaYkzW3Mi1Hoo42AFQFvs9UXg+T8jLsryjKA2AOsFYI8YcQYh2gVxTlbra8kUOCgsJo2VKdAOXkVItLlxSjLSzsPC4ujmi1Wmxs9AhRgcuXr9OwoTM//DCFdu26Ex5+MV+1tWqVru3y5Wsm2s7h4uJk1Fa5cgUuX1aeeRxzc3Pq1KlFs2Zf0bv3SIQoT5AhcpxTQoLP0Ny1EQC1HWty5cp1oy38zAXqOjug1Vqit9FRSZRX19VuRbs23WjXphsPH0bRt+dIzM3Nqe1ox6etujKo3xgqVrIlJDg8q2JfrCsonOaGCZ4OjnZcMfFJeNgFnOtl0hV2njr2Lfnsk2/47JNvePgwij49R7L2x800bfgFn33yDUsXrWXP7sO56vQDBAedwdWgzTGTtjNnzuPs4ohWa2k8165cUVi0eDoNGqq5pbExsaSkpGBjoyc41MM4atOwkXOuc/0zazM9l8LCzlGvXiZtlxUWL5lOwzRtsXGkpKQ869C5JjQknGauDQGwd6jJNZNz7eyZi9Rxrm2s04qVbLl29Qang8/QzHB+1nVxQLl2M1+0/TdwLvQCDZqpA6417Kty41rm5vtpbCt9xA/rZjFu4BROGNKE8pqzoReob9BVPdu6phrTl/KDxIuX0NatA0CRqh+TGJEe3LF4/z1KrV4OZmZgbo5ljWokXr+B1tGB2J9+5uGosZCaQnzomXzRFhJ8hhaGc9vB0Y6rpu1I2HljG2djaOPOhJ3Hyd6VT1t/zaetv+bhw6g87/SD2nGv0URNOLCtVZHfDT1RNAAAIABJREFUM93Ch68bx2//z959R0dR9WEc/86mV3oRlQ4DIQRI6L2KioKChSK99yIECL1DKNK7KCBFX1Skl9CLIAEBaUNJQpGiUtLZzZb3j42bYmiyMQF+n3P2kN17Z/bZ2WHmzp07s+cjWB60yDaspkm/T3irQyMA3ixZgHs3/7J7LoDLoRconSLbtRTlvZYEcuN8BN8ELbZlu3wsaZo3Shbg3u/pky25iNCLlKhTFoD85YpyW7tuK8tV+DVaL+wPgCnBhMmQgMX8cgyOf9Xu6vOve/wTx/GHYB0ukxdrI38f1p51f8A18TlYG96pu2z+7jFfBtQHViQ+umMd2vM4aXXL/L10HbEO/9mYqjwhjff++7MEJY7lbwK8C+xUVXWEpmkTn5Djuf300zbq1q3Onj0/oCgKXboMpE+fTly5EsHmzSHMn/8Vu3b9D0XRMXr0NPR6PVOnjsTZ2YklS6wX3Vy8GEbv3kHpkq1evRppZLvK5s07E7OtQ6fTMWrUVPR6fZrzMZlMGAwJ/PzzZh4+1DNr1hLuPkevOsDmjTupXacaW3auRVEUencfSvee7QkPu8q2rbtZsnAlm7avQacoTBg745FjcU0mEwkJCeza/yN6vZ75c5Zx796/z7Zp4w5q163GtpBvURSFXt2H0KNXe8LCrrJty24WL1zB5u1r0Ol0TBjzRbqNEU7Lxg3bqVO3Ojt3/Q9FUejeLZCevTsSdiWCrVt2sXD+12zf+R2KTmHsmOno9QYWLPiambPGM3hIb8wWCwP6jyQqKpqxo6exeetq9HoD+/YeZsf2vc+VbcOG7dStV52Q3eus2boOolfvjoSFXWXL5hAWLFjOjpDv0Ol0jBk9zZpt/tfMmj2BIUP7YDab6d9vhH0WVCpbNoZQs3ZVNm5fjaIo9OsZRNeebQkPu8aOrXtYuugbftr6DYpOx+TxM9HrDcyavpjpc8axaccaEoxG+nQb/OQ3eknt2rKPyjUrsmLjYhQFRvSbQOuuzbkefoO9Ow6mOU3foO44uzozeLy1oRETFZPigmB72L1lH1VqVmD5xkUoisLIxFzXwm+w7xG5+iTmChzfLzFXbIoLgu3l4f4DOFcIsDbwFYUHE6fg8enHGG/8jv7QYeJ3hJBj0TwwGonftgNjeASKmxtZhgaCIQFjRASR02faPRfApg07qFO3GttDvgNFoVf3wfTo1YHwsKts3bKLRQtWsGXHWnQ6HePHPHrba28nth+lVA0/hn0/ARSFLwfN462O7/PH1VvoHHSolXxwdHakdG3r3WjWBa9i8/wf6TKzL351AzAbTSwdODddsv26/Rd8avgx+PvxKIrC14Pm0aDje/xx9TY6Bx3FK/ng6OyEb2K2H4JXc2BtCK3Gd2boj9bP882wxemSLbmz249RvEZpenw/BkWB7wYtokbHd7l79Q7nQo5z6/w1ev44FiwWtL2nCDt6Pt0zCftTLM9xOXPi/fXbAVmBPsBBrL3vPwEumqY1U1X1FjBF07SZyaabCZwG/pdYv4GmabsTy/ICN4GOmqZ9parqKOB9TdPKJ5Z/DXhqmvZRsvmFAps0TRutqurPwFFN0/olKw8CzJqmTU49vaqq+bEeKPTTNE2f+NpE4ANN03yS11dVtRawF+sZgaTBoU/g5lYgUx4WP893n948nTPvTyWYM+lyM5qfPEwho2TWZQaZd127fnnzkytloADf9Ln14fNSMvZGcI+1reh/f1elp+Vz8vqTK2WAD3KWyegIj+SYSe+Ini0T37AxOGJNpvsPejRf03TdQVW6+UOm+szPu3ZsxXqRrAk4omlagqqqh4HmWC+QBQgGRiYeABwDWgK9gFrAQyAWaKqqajiQD+sdfhTg7y1kDJBXVdXCQMrzY2kLBtaoqnoB2In1jMQYoPUj6v9J4th/VVWnA95YhykdS6Pu3439AFVVjz9L418IIYQQQoiM9FyHq5qm3QTOA6HJLnbdh7XhvjXx+SxgGtYG+Tmst8/8SNO0Q5qmJWA9EKifWLYC2IH1ouCAxOnXAdGJ5U/8xQhN037EeuvQAYnTDAJ6JLvYN3X9eOA9rHcAOgmEAGewnsFI7Tesd//ZAXR5UhYhhBBCCJF5WdL5kdk811Af8XRkqM+zy6zDLyDzDluRoT7/TmZd12Soz78jQ33+HRnq8+xkqM+zy4xDfQ6/1ixdd1BVb32fqT5z5lxrhRBCCCGEEHaVeQ8LhRBCCCGESEeZ8Zab6Ul6/IUQQgghhHgFSI+/EEIIIYR4JaXPTz9mXtLjL4QQQgghxCtAevyFEEIIIcQryZKJ7wSWHqTHXwghhBBCiFeA9PgLIYQQQohXkjnz/sxMupAefyGEEEIIIV4B0uP/HyibvXBGR0jTe475MjrCI31x/1hGR3iketl9MjpCmrb+eTqjIzzSQ6MhoyM8kpuTc0ZHeCEdP7MqoyOkzZSQ0Qke669m3TI6Qpp8sxTI6AhpGuoWm9ERHimz3v89Z7G4jI7wQjG/YmP8peEvhBCZUIBvq4yO8EiZttH/Anj3cuY7CPZ0cM3oCEKI/4g0/IUQQgghxCtJ7uojhBBCCCGEeOlIj78QQgghhHglyS/3CiGEEEIIIV460uMvhBBCCCFeSTLGXwghhBBCCPHSkR5/IYQQQgjxSpIx/kIIIYQQQoiXjvT4CyGEEEKIV9Kr1uP/UjT8VVX1A3JomrYno7MIIYQQQogXw6t2ce9L0fAHfgKmAy90w19RFAZN6kdRnyIk6BOYNGgqNyJupqiTNXsWFm+Yy2f1OmDQJwCwIfQ7rof/DsCZ42dZMHlpeoTjnfHtye2TH5M+gc2Dl3L/6h1bcUCbBvh9VAMscGDWj1ze/StObi58MLsnrlk9SIjTs6H/AuLuRdspjsLUGaMpVboEBr2Bfr2HER52zVbeuu0ntG3/KUaTiRlT57Nj215bWZWq5Vm4dDplfGoB8NEn79OjVwdMZjOrV67jqy/X2C1jp/HdKOhTkAR9AgsHz+X21du28kYdG1Pt/RoAnNgTyrpZ3+Lu5U7vmf1x93TH0dmR5eOWcfGEZrc8M2eNw7d0SQx6Az17DCEs7KqtvF375nTo2AKj0UTwlLls27qbN97Ix4KFU3B0dERRFHr3CuLSpTCat/iQfv26EBUVzTffrGPF8u+eOsPcOZMo4+eDXq+nS7dBXLkSYSvv2KElnTt/hsloYuKkWWzeEkKOHNn4ZsU83NxcuXnrDh079Sc+/mGadf/Wp3cn8ubNRdCwSeTJk4vV38y3lZUpU4qgYZNYvGTlU2cOnjGaUr4qBr2B/n2Gp1jXPmv7MW3bN8doNDJj6gJ2bt9L1mxZOHJ8OxfOXQRgy6YQFi9cQYdOLWneqikWi4VpU+axc/vep8rwNBmHTR6EWqooBkMCowdM4nrEjRR1suXIyoqNi2lW5zMMegOeXh5MmjcaDy8PnJwcmTpqNqePn7FLnmdx+uwFZixYxtdzg//T9zWbzYybsZCLV8JxcnJibGAv8r+Rz1b+5arv2bJrP57ubrRv2YzaVStw4+Zthk2ciQV4LU8uRg/qhZuri92zKYpC0OSBFC9VFIPBwNgBk7ke8XuKOtlyZOXrjQv5uE4bDHoDru6uTJo/Gu+s3sTHxTOi9zju332QLtkGTOqbuJ8yMGXQdH5PYz81f8Ns2tXrhEGfgE6no9eo7pQoUxwnZye+mrGCwyFH7B2M3CN74VKiMBZDAndGfEHCtVu24lxB3XHz98EcGw/AzZ6jMcfEAeBW3pe8wYMJr9vavpmSZcszqqct2+3hM1Nkyz2sW4psv/cYkyLba1MDCavTJt2yeXTvj0OhopBgIGbOVMy3ktY1p4BKuDVvC4Ap7BKxC74AF1e8Bo5A8fLG8jCemBkTsERFpk8+YXcvS8P/pThcq/l2dZxdnOnSuBel/EvSe2QPBncYbiuvVKsCPYI6kz1nNttrbxTMh3bmEoPaDUvXbGrDABxcnFj+4WjylStK/eGt+F/nGQC4ZfMkoHV9lr4ThKOLE11DgplT5VfKtqjDrd/COTj7R/w+qkm13h+wc8zTNbae5N33GuDi6sI79T8loEIZxk4YQusWPQDInTsnnbu1pn6tpri4urB5+xr27j6EwZBAvtfz0qN3Bxydklb9MeMHU61SI2Jj4jh0bAs/fL+ZyAdRz52xQsNKOLs4MezDwRQrV5w2wzsQ3HmiNeObeajxQS2CmgzCYrEwdt0kftl+hMrvVOW3Q6fZsmwj+Qq/Tt85nzO40YDnzgLwfuO3cHFxoV6dZlSoUJaJk4fR/JMu1jx5ctK9e1tqVG+Cq6sLO0O+Y/eug4wYOYBFi1awaeNO6tWvyZixg+jdK4iRoz6nWpVGPHgQxabN37B3zyGuXfv9CQmgSZO3cXV1oXrNxlSq6M/U4JE0bdYBgDx5ctGrVwcqVX4XV1cX9u39kZ0h+xk+rD9r1q5nxcrvCBzUky6dW7P22/Vp1tXpdCxaGEzFCv78uH4zAHfu/Em9Bh8DULlSAOPGDmbpl6ueerm9+159XF2cebdBcwLKl2HM+CG0aZlsXevamga1m+Hi6sKmbavZt+cQfmV8+HHdJoYGjrfNJ3v2bLTv1JI61T/AxdWFQ0c3U7ZU7afO8Th136mJi6szrd/rgp9/KQaO7k3fdoNt5VVrV6LvsB7kyJXd9lqbbi04eiCUb5Z8S8Ei+ZmyYCyfvtXOLnme1rJV/2Pjtt3p0nh+kl0HjmAwGFi1YCqnzl5g6rxlzJlk3d5evBLB5pB9rFk4DYDPegRSyd+P6Qu+5pMm79CoQS3WbdrBim/X07Xtp3bPVuedmji7OtP2va6U9i/FgNG96d9uiK28Su2K9BnWnezJvs+mrRpz/rTG4hlf8f6n79KpX1umjphl92w13q6Gi4sz3Rv3xse/JD1HdiOow0hbecVa5eka1CnFfqrhRw1wdHKgxwd9yZk3J3Xeq2n3XJ71q6K4OHO9RX9cy5QgV2AXbvYaYyt39SnKjU7DMKfatjvmzUm2ds1QHB3snikpWxUUF2euNR9gzTa4Mzd7jrWVu/gU5UbH4ZjSyta+abpmc65cHZydiRrUA0fVB48OPYiekNiecHPDvX03ooL6YYmKxLVpCxTvLLjUboDxykXi1y7Hpd7buH3ahrglc9ItY3ozvxQtyKf3wjf8VVXdCxQA5qiq+hHQHJgNvAPEAJuBzzVNi0ysbwFaAEMBFQgFPgMGAa2BKGCopmkrE+tHJM7vY6AscBLoqWnaCXt/ljIVS3Nkzy8AnD1xnpJ+xVOUm81mejcfyNdbFyV9fj+VXHlzMvd/M9DH65k1Zj7Xrly3dzTerKAStu8UADd/vcxrfoVsZfH3Y1jy9lAsJjMeb+TkYZS1p+LYsm0oOuv/KO98OYj9y349ApWrBLA75AAAx4+domy50rYy/wA/fjlyAoMhAYMhgfCwa5TyLcG5sxrTZ46lf58R7Nr/g63+2bMa3t5eGI1GFEXBYrHYJWPJCj78uu9XAC79epEifkVtZXdv/cWENqMxm62jCx0dHUjQJ7Dpyw0YE8/k6Bx0JCT+bQ9VqpQnZOc+AI4dO4m/f9IyK1++LEeOHMdgMGAwGAgLu4pv6RIEDZ1AZGS0LePDh3oKFsrP6dPnuH/f+n0eP36aihXLPVXDv3rVimzfYT0xd/SXEwT4+9nKKlQoy+HDobYMV65E4Fe6JNWqVWTyFOtOZdv2PYwfO4QrYRFp1r18JYJvvlnHrl0HKVGiyD/ef+bMcbRp29u23J9GpcoB7NqVuK6FnqJsOV9bWbkAP345+muKdc3HtwRlyvriV6YUP21eyV9/3SMocDx37vxJ7WpNMJlM5M/9OpGRz39wactRsQyHdlt7UE+fOItPmZIpys1mM10+6c3aHV/bXlu5aC0Gg3X9cnB0QK832C3P03oz32vMnDicoWOn/ufv/etv56lWyR+AMqVKcFa7bCsLu3qDCmVL4+LiDED+N/Jx8Uo4VyKuMzqwFwDlfEsSPDcdzq4C5Sr6cTjx+/ztxFl8ypRIUW42W+j2SV9W71hme231ku/Q6az363jt9Tzc/fN+umTzq1iao3uOAXDuxHlK+Kn/yNa/eSBLty6wvVaxVnnCLoQTvGICoDBzhP0biW7+pYg7GArAw1MXcPUtllSoKDgVyEeesX1xzJGVyO+3E/XDDhRnJ3KP7sMfI2eRf136NVzdAkoRe+D4I7M5F8hHnrF9cMyZlQfrdtiy5RndmzsjZ1Pg+9npls3Rx4+E49Z2h1E7h2OxpO/TqYQvpohw3Dv0wCFvPvQ7NmGJiuThhnWQuK7pcuXB8iB91jWRPl6Gu/o0BW4AQYl//4D1c1UB3geKAGtTTTMZ6AdUBvIDJ7A2+CskTr9IVVXPZPXHASsBf0ADdqiqmh078/B0JyY61vbcZDbj4JD0FR07cJyo+ykbC3fv3GXF3NX0+ngAy+esYtTsIHvHAsDF0w19dLztudlkRkmWzWIyU75tA9qtH8OFLb8kvW620GpNEOXbvcWVPafslsfLy5OoqKRhQyaTCQcHa6+Il3fKspiYWLy8vZgybRTzZn/J7Vt3UszrwrlL7Nr/A4eObmHHtj1ERdpnOJKbpztxyb5Ps8mMLnGZmYwmou9b36f1sHaEnw3jVvhN4qJiMegNZM2VlT6zBrB6ygq7ZAHw8vYi8lHLzMvT1sAHiI6Oxdvbi7t372M0GilWrDATJwYxaeJsrlwOp2TJYuTOnRM3N1dq16mKu4f7U2bwTLF8TSazLYO3txdRUUnrd3R0LFmyeOOdLFt0dAxZsng9su6DB5HsDNmf5nu/914Dzp27yMWLV54qa/LM0ZExyTKnXG6p1zVvb08uXQxjyqTZNGnUmi2bQpg0dbht2o6dW7F117ds/Gn7M+V4HE8vD2KikzKak2UEOLL/GJGpth3RUTHoH+rJkSs7E+eOZtbEBfzXGtSpjqNjxvQ/xcTG4eXhYXuu0+kwGk0AFCtcgOOnzxAbF8eDyChOnrlA/EM9JYoVYu/BowDsPfQL8Q/16ZLNw8sj5b4g1fd5NI3vE6wHeIvWzaZ5x2Yc3PVz+mRLtZ8ym00p9lOhaeynsmTPwhuFXiewzTBWz1/L0BmBds+l83THlCyXxWSGxFyKmysPVm3gdmAwNzoPJ2uL93AuXojcI3pyf9k6jH/ctXueFNk83DEny0bybO6u3P9mA7cCp3K98wiytWyES/GC5B7Rg3tffZ/u2RR3dyxxyZab2Qw667qmeGfBya8scV8vImp0IK6NP0aX7w1rRbMZ7/Ff4PpeUwyhdh629R8zo6TrI7N54Rv+mqbdA0xANFAG8ANaaZp2RtO0UKAV8Laqqr7JJpunadoeTdNOApuwnhkI0jRNA2YAbkChZPXXapo2X9O080BXwAjY/fxubEwcHp5JDSidTofJ9PieyfOnNPZvPwTA6WNnyJU3p71jAaCPicfZw9X2XNHprBvWZEKX72RWhZ68WUmlQBUf2+urWkxk5cdjabawr93yREfH4OmZcqdtMll32tFRMXh6JZV5enqQYDBQuWoAg4b04qfNK8mWLQtLvvoCn1IqDRrWxr90Xcr51iFXrhw0/uBtu2SMj4nDzcPN9lzRKZiTLTMnFyf6zh6Am4cbS4cnncXJrxZg5OpxrA5eybmjZ+2SBSA6Khovz6Tj2RTLLDoGr2TLzMvLw9YjXbNmZdZ+u4hOnQZw6VIYDx5EMWTweL5ZPZ8FC4M5dfIsd+8+XY+P9btJO0NUVDSeyfJ5eXnw4EEkUcmyeXl58uBB1CPrPk6rls1YuvSbp8r5z8yPWNdSrYeenh5ERkZzYP8RDu63NhC3bNqJr1/S/4cvl6zCt3gNqlSrQLUalZ45T1piomNxf8T/h8cpVqIIS9bNYc6kBRz/+Ve7ZHlReHq4ExuX1JlhsVhwTBxSUaTgm7T4sBHdBo1h6rxl+PkUJ2sWbwb16MCeQ7/QdeAoFJ1C1ize6ZItNjoW93/sC578fQJ0/agPHZr0YNqXE9InW0wc7p7Jt2tP3k9F3Y+yjek/eeQ0bxZ+w+65zDFx6JJtb9Ep1gY2YHmo5/6K9Vge6rHExRN39BSuJYvgFuBLjp6f8cbyYByyeJF3+pBHzP05s8WmzqZLyhav5/7Kn6zZYuOJO3IKF5+iuJf3JWfPVry5YgoOWbx4LZ2yWeLiUNySddwoCpit65olOgrjJQ3Lg3vwMJ6Es6dwLJx0tiJqeH8ih/TGa+jY1LMVmdgL3/BPpRTgDtxVVTVGVdUY4GJiWfLzkZeT/R0HRGia9vf4joeJ/yYfdHrg7z80TdMDp4HkBxJ2cfrYGarUtTYESvmX5Mr5sCdO03FAWz7t9BEARX2KcOfmH/aOBcD10IsUqVMWgHzlivKnljScKHvh12i2qB8A5gQTJoMRi9lM1R6N8f2wOgCGOH2KRu/zOnrkOPXfsl6cG1ChDOfOXbSVnTh+mspVyuPi4oyXtyfF1SLW1wLepkmj1jRp1Jr79yPp3L4/UVHRxMc/5GG8HrPZzJ9/3iVr1ix2yXgh9Dz+dQIAKFauONe0qynKA5cMI+JcBIuDFtiGnrxR7E0GzA9kVp/pnNxr39FkR34+zlsNawPWYTVnzyZdNBwaepKqVSvg4uKMt7cXqlqUc2c1atasTPDUUXzQpB2/nvgNAAcHBypWLEfDBp/SudPnFC9ehCM/hz5VhkM/H+Odt+sCUKmiP2fOnLeVHTt2kurVK+Li4oK3txclShTjzFmNw4eP8c7b9QB4u2EdDh46+si6j+NfrjSHnzJncr8cPUH9BtYxyQHly3A+2br26/HTVK4SkGJdu3DuIjPnjOf9Jg0BqFGrCqdPnqVI0UJ89Y11OEFCQgIGvcHau2YHJ4+dpka9KgD4+Zfi0oUnn9UoXLwg05ZMYEj3URzc/WL32P0b5XxLcuCIdX04dfYCxQoXsJXdexDJg8hoVs6bwpA+nbn9x18UK5Sfw6En6d6+OYumjUGnKFQtXzZdsp089hvVE7/P0v6luPwU32eH3q1p9JF1nYuPe4j5KQ8UntVvyfZTPv4lCTsf/sRpTv/yG5UTpyniU5g7v9t/PxV/4iweNSsC4FqmBIaLEbYy54Kvk3/VdGuD29EBN/9SPDx7iYh3O3GjbSA32gZiiozm9ueT7Z7Lmu0cHrUq2LLpLyYtM2u2aUnZAkrx8MxFwt/pzPU2g7neZjCmyGhupVM24/nfcCpv/W4cVR9MV5OyGS9rOBQohOKdBXQO1vJrEbh+1ArnOm9ZKz2MBzttxzKKJZ0fmc0LP8Y/FUfgKtAgjbLk4ztSD5x+0lprTPVch/Usg13t23qAijUDWPzTHFAUJvSfQvMuH3Mj/HcO7jyc5jQr561m1JxhVKtXGZPJxPh+6bNx0LaFUrh6adr+MAoUhU0DF1Gx0zvcj7jDpZAT/HHuGu1+HIMFC1f2nOLa0QvcvXKT96d3o+yntVAcdGwatNhueTZv3EntOtXYsnOt9W4z3YfSvWd7wsOusm3rbpYsXMmm7WvQKQoTxs545PjlG9dvsuKrtWzesQaDIYGI8GusWfVDmnWf1S/bjuBXvSzjf5iCosC8gbN5r1NjbkfcRuegw6dSKZycHSlX2zrOeHXwSj7o3gwnFyfaj+oEQFx0nO2C4Oe1YcN26tarTsjudSiKQveug+jVuyNhYVfZsjmEBQuWsyPEOk54zOhp6PUGpgSPxNnZicVLrBc5XroURp/ewzAYDBw8vJGHD/XMmbX0qXv816/fSv16NTmw7ycURaFj5/7069uFy1fC2bRpJ3PnLmPvnh/Q6XSMGDkFvV7PxEmz+OrLmXTq2JK/7t7js9Y9iYuLT7Puo+TMmZ3omNhHlj/O5o07qVWnGpt3rEFRFPr0CKJbz3aEh11j+9bdLFm0ko3bVqPTKUwc9wV6vYFxo6cza+5E2ndsQVxcPP17D+fOnT85+9sFtoZ8i8ViYdfOAxw+dOxfZUpt15Z9VK5ZkRUbF6MoMKLfBFp3bc718Bvs3XEwzWn6BnXH2dWZweP7AxATFZPiguCXXb2alTkcepJW3QMBC+OG9GX5t+vJ//pr1K5WkRs3b/NplwE4OTryefd2ODg4UCj/64yYPAdnJ0eKFsrPsP7d0iXb7i37qFyzAl9vXIiiKIzqN4HPun7K9fDf2feI73P9mk2MnT2cD1q+h07nwKh+9tlupLZ/60HK1wxg/k+zURSFSf2D+bTLR9wI/51DO9MeXrRx9RY+n9SXhRvnoKAwfchMu+eKCTmMe1V/3lw9AxSF20HTydq2KQnXbhK75whRm3aTf+1MLEYjUT+FYLh89ckztVe2nYfxqFqO/GumW7MNnUG2dh9iuHqT2D1Hid64h/zffgFGI5Hrd2G4fO3JM7UTw88HcCpbHu/geaAoxMyajGuTTzDdukHCL4eJW74Y7zHWa3D0B/dguhaOOeoBnv2G4trgXdA5EDMrfdodIn0o9rqQMSOpqhqO9Xael4ANQH5N0+4kluUH5gADNE27knhx7/uapm1KLJ8GlNc0rXbi87zALaCCpmmhiRf3/qBp2oDEclfgGtYLgL98mnxVXq+TKRfye475nlwpg3xx3z4NovRQJ1vJJ1fKAFv/PJ3RER7pofG/v3D0aWVz83xypQzwmpvdLyOym+Nnnv6uSP85k/0uiE8PFct0yOgI/+Dp4PrkShnkS2+njI7wSBZL5hu/DZCzWFxGR3ikHBv3ZbqF9kPelunaRmt6e3Wm+swvS49/DFAS+A44C6xVVXUg1p76OUB2IOI55t9VVdVQrBcBB2E9Y/B0Ny4XQgghhBAiE3hZxvjPwnorzu1AY+Ae1h/z2gfcBd7VNO15huZ8CQzA2vB/HainaZp9bv0ihBBCCCEyhFlR0vWR2bwUPf6api0Fkt9Qudlj6iqpng9M9fw2//xBsIuapvV53pxCCCGEEEKRfyl0AAAgAElEQVRklJei4S+EEEIIIcSzypQXYaajl2WojxBCCCGEEOIxpMf/CTRNK5jRGYQQQgghhP292L9C8Oykx18IIYQQQohXgPT4CyGEEEKIV5I58914J11Jw18IIYQQQogMpqqqI9YfpG2JtY2+EhioadpjfwVTVdUuwKLUd65MizT8hRBCCCHEK8n8jzu4Z6iJwDtYf5PKGVgOGICBj5pAVdU3galP+wYyxl8IIYQQQogMpKqqK9AD+FzTtJ81TdsH9Aa6qarq9phJlwInn/Z9pOEvhBBCCCFeSZZ0fjyDsoAHsD/Za/sSXyub1gSqqnYCsgMznvZNpOEvhBBCCCFeSWYlfR/P4HUgVtO0yL9f0DQtCogD3khdOXGIzySgA2B62jeRMf7/AV3mGj9m45BJcwGYLZn3t/QcM+ly0ymZMxeQSZdY5qZk5qVmSsjoBI/m4JTRCR7LUeeQ0RH+wV3nnNERHiPz7gsUJXNmU6RL90XlDujTeF0PuKTx+hJgrqZpv6mqWuBp30Qa/kIIIYQQ4pWUiX7AK560G/guWHv9bVRV7Qi8hvVi4Gcix4VCCCGEEEJkrBuAh6qqXn+/oKqqN9YzAb+nqvsZUAK4r6pqDPC/xPoxqqq2etybSI+/EEIIIYR4JWWiAVungFigBrAl8bVaia+dSlX3MyD5nX7qAIuxXgR853FvIg1/IYQQQgghMpCmafGqqi4B5qqq2hbr5XFzgHmapj1UVTUL4Kxp2p+apqU4A6CqaonEeVx+0vtIw18IIYQQQrySnvHOO+ltCNae/I2AEVgFDEssmwXUBgo+zxtIw18IIYQQQogMpmmaHuiW+Ehd1u4x023iKW+gJw1/IYQQQgjxSspEd/X5T8hdfYQQQgghhHgFSI+/EEIIIYR4JUmPvxBCCCGEEOKl88L3+Kuq6gfk0DRtT0ZnEUIIIYQQLw5L5rqrT7p74Rv+wE/AdOCFb/grisLASX0p6lMEgz6ByYOm8XvEzRR1smbPwsINc2hTryMGfQIA60O/43r4DQDOHj/HwslL0yMcDce3I7dPfkx6I1sGL+XB1aTfiPBvU5/SH9XEYrFwaNaPXNl90laWvchrtFk/hjnle2JKzPz8cRSmfTEG39Il0OsN9O0VRHjYNVt5m3af0LZ9c0wmE9OC57NjW9LqUaVaBRYvnU7pkjUBKOdfmvGThqIoCn/c+YuunT5HrzfYJWOH8V3J71MQo97I4sFzuXP1tq38nY7vU/X9GgCc3HOc72d9i4ubC71mD8Azqxf6uIfM6z+T6HtRz53l7zwzZo6jdOIy691zKGFhV23lbdt9SvuOLTEZjUydMo9t23aTO09Oli6bibOTE7dv/0H3roOIj39I4yZvM+DzblgsFr5atoYVy7/7V3nmzpmEn58Per2ert0GceVKhK28Y4eWdO78GUajiYmTZrFlSwg5cmRj5Yp5uLm5cvPWHTp16k/x4kWYPm20bbpKlfxp9lFHjh07ybmzBzh79gIAP/20jTlzv/xXOYNnjKaUr4pBb6B/n+Ep1rXP2n5M2/bNMRqNzJi6gJ3b95I1WxaOHN/OhXMXAdiyKYTFC1fQtUdbPmzWCICQHfuYNmXeM+d5VMZhkwdSvFQxDAYDYwZM4npEyh96zJYjK8s3LuKjOq0x6A14enkwcd4oPLw8cHJyYtqo2Zw+fsYueZIzm82Mm7GQi1fCcXJyYmxgL/K/kc9W/uWq79myaz+e7m60b9mM2lUrcOPmbYZNnIkFeC1PLkYP6oWba1q/ZJ++Tp+9wIwFy/h6bvB/+r6KojB40gCK+RQhwZDA+IHB3Ej1fWbNnoUvNyygRb12GJJtrwoUzc/XmxbSsMwHKV63Z7beE3pSyKcwCYYEZgbO5GbErRR1smTPwhfrp9O1QXcS9Am4e7kTNG8Iru6uGA1GpvSdyv0/79s7GLlH9sKlRGEshgTujPiChGtJuXIFdcfN3wdzbDwAN3uOxhwTB4BbeV/yBg8mvG5r+2Z6QbK5d+uPY8GiWBIMxM6divl20rrm5F8Jt+ZtATBeuUTcoi/AxRXPz0egeHpj0ccT+8UELFGR6ZNP2N3L0PB/aY7Var5dHWcXZ7o27k0p/5L0HtmdIR1G2Mor1ipP96DOZM+Zzfba6wXzoZ25xOB2w9Kapd0UbxiAo4sTKz8cQ75yRag3vCXfd/4CALdsnpRrXZ+v3hmGo4sTnUKmML9KXwCcPd2oO7wVJoN9Gvx/a/R+A1xcXWhY7xPKVyjLuIlD+ax5dwBy585Jl25tqFuzKS6uzmzdsZa9uw9hMBh4/fW89OzdAUenpFV/5pwJtGvdi/Cwa7Ru+zFv5n+dy5fCnztj+YaVcHJxZtSHQyharjifDW/P9M6TrBnfzEP1D2oxvEkgWCyMWjeRY9uPUKqqH+G/XeGH2d9R86O6fNj7Y1aMefbGalree/8tXF2dqV/3IypUKMuESUG0+LSrNU+enHTr0Y5a1Zvg6urC9pDv2L37IAM+786aVd+zZvWPDA3qS4eOLVkw/2vGjA2kVo0mxMTEcuz4DjZt2sm9u8+2I2/S5G1cXV2oUbMxlSr6Exw8kmbNOgCQJ08uevXqQKXK7+Lq6sK+vT8SErKf4cP6s3btelas/I5Bg3rSpXNrZs1eQv0GHwPQrNl73Lx1hx079lK3bg2+/XY9/fqPeFyMJ3r3vfq4ujjzboPmBJQvw5jxQ2jTsod1ueXOSeeurWlQuxkuri5s2raafXsO4VfGhx/XbWJo4HjbfAoUfIOPPmlMw7ofY7FY2LRtNVs2hXDurPZc+QDqvlMTZ1dn2rzXhdL+pfh8dB/6tRtsK69auxJ9h3UnR67sttdad2vO0QOhrFryHQWK5GfKgjE0f6v9c2dJbdeBIxgMBlYtmMqpsxeYOm8ZcyYNB+DilQg2h+xjzcJpAHzWI5BK/n5MX/A1nzR5h0YNarFu0w5WfLuerm0/tXu2x1m26n9s3LY7Qw44ar9dAxcXZzo27oGvvw/9RvVkYPsgW3nlWhXoNawr2XNlSzGdh6c7/Ub2xGDn7W1yVRtWwcnVmf4fDKBEuRJ0GdGZ0R3H2soDavnTYUgHsibbT731cQPCL0Tw5cRlvNPibT7u1ozF4+zbQeVZvyqKizPXW/THtUwJcgV24WavMbZyV5+i3Og0DPODlB0pjnlzkq1dMxRHB7vmeVGyOVWqjuLkTNTgHjgU98G9Qw9iJia2J9zccGvXjehh/bBER+L6YQsU7yw412qA8cpFHn67HOe6b+P2SRvils5Jt4zpTcb4Z0KqqlZQVXWXqqoxqqo+VFX1F1VVq6qquhcoAMxJ/BtVVfOqqvqdqqrRqqreUlV1aeKvnf09L4uqqs1VVT2VOK+DqqoWVFV1nqqqUaqq3lBVtXWy+hGqqg5QVfVnVVXjE//1T4/P6VfRlyN7jgFw9sR5SvipKcotZgt9mw8i6kG07bUSfsXJlTcnc/43nWkrJpG/yJvpEY03KqiE7TsNwM1fr5DXr5CtLP5+DMveDsJsNOGRKwsPo+JsZW9P6sD+4O8wxtu356lylQB279wPQOixk5Qt52sr8y/vx9EjJzAYDERHxRAWdpVSviouLs5MnzWOQf1H2+oWLVaIe/fu061HOzZuXUXWbFnt0ugHUCuU5NS+EwBc/vUihf2K2sru3vqLyW3GYDGbsVgsODg6YNAnsHXZRn6cuw6AnPlyEvmX/XpRqlQtT0jiMjt27CTl/EvbygICynDk5+MYDAaioqIJuxKBr28JhgSOY+2a9SiKwutvvMYff/yF2WymvH8DoqKiyZ4jG4qiEBsT+8x5qlWtyPYd1jMxR385QYC/n62sQoWyHD4castz+UoEfqVLUq1a0jTbt++hbt0atmnc3d0YOfJz+ic29P39S1OuXGl2haxjzZpF5M2b+9kXGlCpcgC7dh0A4HjoqRTrWrkAP345+isGQwLRUTGEh13Dx7cEZcr64lemFD9tXsmXy2eRJ08ufr9xm0+bdsKc+J07Ojny8KH+X2VKrVzFMhzefRSA306cpVSZEinKzWYzXT7pQ2SyhsU3i75l3cr1ADg6OtjlLFdafv3tPNUqWTeZZUqV4GyyH5YMu3qDCmVL4+LijIuLM/nfyMfFK+FcibhO9coB1s/mW5ITv51Ll2yP82a+15g5cfh//r4AZSqW5vBe6/d55sQ5SqbaF5gtFnp+OoCoVA3FoKmDmD95MQ/jH6ZbtlIVSxG69zgAF369QDG/YinKLWYLQ1oMJTrZfir8QgTunu4AuHu5Y0ww2T2Xm38p4g6GAvDw1AVcfZPlUhScCuQjz9i+vLlqOt5N37K+7OxE7tF9+GPsXLvneVGyOfn4kfDrLwCYLp7DsWjSuuZUwhfT1XDcO/TAa+IczA/uYYmKRL9xHQ//txIAXa48mB/Y+eyNSFeZvuGvqqonsBU4CZQBKgPRwCKgKXADCEr8G+AHrJ+rCvA+UARYm2q2k4F+ifPKD5wAooAKidMvSnzfv40DVgL+gAbsUFU1O3bm4elObHRSA8pkNuHgkPQVHTtwnKj7KTf0f925x8q5q+n98eesmLOKkbOH2jsWAC6ebuijkxr0ZpMZJVk2i8mMf9sGtFk/Gm2LdSNSvV9Truw+yR/nr/1jfs/Ly8uTqKikHYvZZMbBwSHNspjoWLy9vQiePoq5s5Zy61bSEKUcObJRsZI/y5as5sP321KrVhVq1qpil4xunu7EpVpmusRlZjKaiL5vzdhqWDsizoZzO9w6rMtiNjN8zVgatmvEyT3H7ZIF/rlcTMmWmbd3qmUWE4t3Fi8AHBwcOHpsGzVqVubIz6GJ05p4v3FDDh/ZzKFDv5CQYHzmPN7enkRGPiqPF5FRSet6THQs3lm88fJKmiY6OoYsiRkBOrRvwfffb+Ju4pkHTbvMmLHTqVf/IzZs2MbMmUm978/Cy9uT6MiYZDlNj17XYmLx9vbk0sUwpkyaTZNGrdmyKYRJU4djNBq5d8+abfT4QH47fY6wZEObnoeHlzvR0WlnBDiy/xiRqbYd0VEx6B8ayJErOxPnjmL2xAV2yZJaTGwcXh4etuc6nQ6j0drwK1a4AMdPnyE2Lo4HkVGcPHOB+Id6ShQrxN6D1obv3kO/EG+nA6Rn0aBOdRwdM+akuIeXB7FRSfsCs9mc4vv8ZX/oP77Pzp+352DIz1w6dyVds7l7uqfMlmy7BnDiwK8pGv0A0fej8K/pz+Jdi/ioazO2rd1u91w6T3dMyfafFpMZEnMpbq48WLWB24HB3Og8nKwt3sO5eCFyj+jJ/WXrMP5x1+55XpRsuLtjiU2WzWwGnXVdU7yz4FS6LHHLFxE9NhDXxh+jy/eGtaLZjNe4L3Bt1JSE40fSN2M6M6fzI7PJ9A1/wB0IBgZrmnZF07STwHzAR9O0e4AJiNY07Z6qqnUAP6CVpmlnNE0LBVoBb6uq6ptsnvM0TduTOK9NQAwQpGmaBszA+nPJhZLVX6tp2nxN084DXbH+jLLdzzvHxsTh7ulme67T6TCZHr/aXDilcWD7IQBOHztDrrw57R0LAH1MPM4eSdkUnc668UrmxPKdzKnQizcrlSB/lZKU+rAaZT6tTcu1w/DIlYXmKwennu2/Fh0dg6dnysaEyWRKs8zTywNDQgKVq5YncGhvNmz5hmzZsrD0qy+4d+8B4WFX0bTLGI1GdoXsp0w533+8378RHxOHW4plpmBOtsycXJzoNXsArh5uLBu+KMW041uMZMzHQfRfmJ7LTLEts6ioGLySLzNPD1vvsNFopGL5hvTtPYxFS6bb6mzcsB21aBWcnZ1o0aopzyoqKgYvr6Tj6+TfYVRUNF6eSWWeXh5EPogkOjoGLy9rTi8vTx4k6/Fs0eJDli1bY3u+Z88h9u61/t9Yv34rZcuUeuaMYG0ge3o95brm6UFkZDQH9h/h4H5rw3XLpp34+vkA4OLizMKl0/D09CBwwBjsJTY6Do/EHtXUGR+naInCLFk3m9mTFnL855NPrP9veHq4ExsXb3tusVhwTBy6UKTgm7T4sBHdBo1h6rxl+PkUJ2sWbwb16MCeQ7/QdeAoFJ1C1ize6ZIts4qNjrX1kIN1XP2Tvs93mjagSYtGLFw3ixy5sjNnzfTH1v+34lLtpxSdLsV2LS2t+rfifwv+R5d6XQlqNYwRi+1/JsUcE4cu2fYWnQKJuSwP9dxfsR7LQz2WuHjijp7CtWQR3AJ8ydHzM95YHoxDFi/yTh9i91yZPRtxcShuKdc1zNZ1zRIVhfGShuXBPXgYj/HsKRwLJZ2tiB7Rn6ihvfEcPPYfsxWZV6Zv+Gua9gewFOipquoyVVUPAitIO3sprAcKdxOHBcUAFxPLkp8rvZzs7zggQtM0S+Lzv8+RJh/YeSBZHj1wGrBP6zCZ346doUrdSgCU8i/JlfNhT5ymw4A2fNKpGQBFfQpz5+Yf9o4FwO+hFylSpwwA+coV4U/tuq0se+HX+HCRdUy/OcGEyZCAxWxhUa3PWd18AqubTyD2z0jWtp5itzxHfz5B/Ya1AShfoWyKcdInQk9TpWp5XFyc8fL2pLhahBOhp6jk35DG735G43c/4/79SDq1709E+HU8PNwpVDg/AJWrVuDC+Ut2yXgx9AJl61iHKxQtV5zr2tUU5Z8vCeLquXC+DFpg7WUBmvRoRvUPrZ/rYZwe81M03p7WkZ+P81biMquQapkdP36KKtUq4OLijLe3F6palHPnNGZ8MZYaNSsDEBMdg9lsxsvLky3b1uDs7IzFYiE2Nt6W/1kc/vkY77xdF4BKFf05c+a8rezYsZNUr14RFxcXvL29KFGiGGfOahw+fIx33q4HQMOGdTh4yNq49vb2wsXFhRs3ki6GX7RoGk2bWi+krVu3Bid+/e2ZMwL8cvQE9RtYLwQPKF+G8+cu2sp+PX6aylUCUqxrF85dZOac8bzfpCEANWpV4fTJswCsXDOfs2c0BvYbhflfLLNH+fXYaarXs56pKu1fiksXntzrW7h4QaYtmcCQ7qM5tDv9euzK+ZbkwBHrmaJTZy9QrHABW9m9B5E8iIxm5bwpDOnTmdt//EWxQvk5HHqS7u2bs2jaGHSKQtXyZdMtX2Z06tgZqtW1/r/z9ffhyoUn7wuaVmtJt4/60u2jvtz98x69W3yeLtnOHTtHhboVAChRrgQRF548NDImMobYxLOfD+5GpjiosZf4E2fxqFkRANcyJTBcjLCVORd8nfyrpoNOB44OuPmX4uHZS0S824kbbQO50TYQU2Q0tz+fbPdcmT1bwvnfcAqwtjscivtgvJr0fRqvaDgUKITilQV0DjiqPpiuR+DarBXOta1DktDHgx23ZRnBks6PzCbTX9yrquprwHHgPNYhP6uB3MCqNKo7AleBBmmU3Un2d+orn5601qYex6DDeqbBrvZtPUiFmgEs/GkOigIT+gfzaZeP+D38Jgd3Hk5zmm/mrWHknCCq1quMyWRifD/7Na6T07aFUrC6L5/9MBJFUdg8cDEVOr3D/Yg7XA45wR/nrtH6x9GAhbA9p7h+9EK65Pjbpo07qF23GttCvkVRFHp1H0KPXu0JC7vKti27WbxwBZu3r0Gn0zFhzBePHL+ckJBAn55BLP5yBoqicOzor+zcvtcuGY9tO0Lp6mUY88NkUGDRwDm826kxdyJuoTjoKFmpFE7OTpStbT04WBu8kr3fhdB9el/qfFofnYOOhYPsd8HUxg3bqVO3Ojt3/Q9FUejeLZCevTsSdiWCrVt2sXD+12zf+R2KTmHsmOno9QYWLPiambPGM3hIb8wWCwP6jyQ6Oobvvv2JbTvWkpBg5OyZC6xds/6Z86xfv5X69Wqyf99PKIpCp8796de3C5evhLNp007mzl3G3j0/oNPpGDlyCnq9nomTZrHsy5l07NiSv+7eo3XrngAUL1aYiKvXU8x/2LCJLFk8g25d2xAbG0/XbgP/1XLbvHEntepUY/OONSiKQp8eQXTr2Y7wsGts37qbJYtWsnHbanQ6hYnjrOvauNHTmTV3Iu07tiAuLp7+vYfz7nv1qVKtIs7OztSrb702YfyYGYQee/6e9t1b9lGlZgWWb1yEoiiM7DeB1l2bcy38Bvt2HExzmj5B3XF2dSZwfD8AYqJiU1wQbC/1albmcOhJWnUPBCyMG9KX5d+uJ//rr1G7WkVu3LzNp10G4OToyOfd2+Hg4ECh/K8zYvIcnJ0cKVooP8P6d7N7rsxs79b9VKpZni83zAdg7IDJtOzyCTcifmf/jkMZmu3QtsP41yjHFz9OB0VhxuczaNr5Q25G3OTIzqNpTrN82gr6B/fj/TaNcHB0ZNbgWXbPFRNyGPeq/ry5egYoCreDppO1bVMSrt0kds8RojbtJv/amViMRqJ+CsFw+eqTZ/oKZEs4cgCnsuXxmjIPBYWY2ZNxbfwJpts3SPjlMPErF+M1eioAhkN7MF0Lxxz5AI9+Q3Gp/y7oHIidnT4HJSJ9KBZLZjweSaKqak9gEFDo7155VVUDgSlYG+BhwHRN0+aqqtoQ2ADk1zTtTmLd/MAcYICmaVdUVbUA72uatimxfBpQXtO02onP8wK3gAqapoWqqhoB/KBp2oDEclfgGjBU07Snut1KtdfrZsqF/L5jvidXyiDB99LegWQGDbP/uyEj6W3zX6czOsIjxSf892O0n1ZWN88nV8oA+dxyZHSERwo99VVGR3g0B6eMTvBYVf3aZXSEf8ju4PHkShlkjmem3H1majmLxz+5UgbJ/tO+THcnxln5P0vXlazvtW8y1WfO9D3+wF0gD9BIVdUzQG3g7wGCLljH55dUVTU3sBM4C6xVVXUg1p76OUB2IOI5MnRVVTUU60XAQVjPGDz7jcuFEEIIIYTIIJl+jD/WBvYS4GusY+u7Ap2xDp0KAGYBrYHtmqaZgcbAPaw/6LUP64HDu5qmPc/QnC+BAVgb/q8D9TRNi378JEIIIYQQIjN71e7qk+l7/BMb830SH8l9m/jvIawX//5d/wbQ7DHzU1I9H5jq+W3++aNgFzVNS/3+QgghhBDiBZYZG+fp6UXo8RdCCCGEEEI8p0zf4y+EEEIIIUR6eNUuH5eG/xNomlYwozMIIYQQQgjxvKThL4QQQgghXknmTHWzzfQnY/yFEEIIIYR4BUiPvxBCCCGEeCXJXX2EEEIIIYQQLx3p8RdCCCGEEK+kV+2uPtLjL4QQQgghxCtAevz/A8fvXs7oCGnKmds9oyM80mvu2TM6wiMZM2n/gMFkzOgIj5TVzTOjIzxSXII+oyOk6XLCTS77F8roGGn6q1k3cn6/MKNjvJAOn/46oyOkqUv5QRkd4RFiMzqAeMmZM+k+Pb1Iw18IITKhzNro/1vFMh0yOkKaHHUOGR3hkTJroz8zq3/n94yO8EhOOqeMjpAmh78y72COixkdQEjDXwghhBBCvJrkrj5CCCGEEEKIl470+AshhBBCiFfSqzXCX3r8hRBCCCGEeCVIj78QQgghhHglyRh/IYQQQgghxEtHevyFEEIIIcQryaxkdIL/lvT4CyGEEEII8QqQHn8hhBBCCPFKkl/uFUIIIYQQ4hXwajX7X9KhPqqqWlRVfS+jcwghhBBCCJFZSI9/BlMUhdmzJ1C6dEkMBgPdugUSFnbVVt6hQws6dmyF0Whk8uQ5bN26y1bWq1dH8uTJxYgRkwEICPAjOHgkiqJw+/aftG/fF71eb7ec3Sb0oGDJQiQYEpgbOJvbV2/Zyht3bEKNxjUBCN0Tyrcz19jKKjesQtVG1ZjRZ5pdsqSVbcSUQNRSxTDoDYwaMJFrETdS1MmWIyurNi3lg9otMegNttfrvVOLho3rEdh9ZLrk6jS+GwV9CpKgT2Dh4LncvnrbVt6oY2OqvV8DgBN7Qlk361vcvdzpPbM/7p7uODo7snzcMi6e0OyWJ7Oua4qiEDxjNKV8VQx6A/37DCc87Jqt/LO2H9O2fXOMRiMzpi5g5/a9ZM2WhSPHt3Ph3EUAtmwKYfHCFXTt0ZYPmzUCIGTHPqZNmfevc/2dbeas8ZQuXRK93kDPHoNTLLd27ZvTsWNLjEYTU6bMYdvW3bzxRj4WLgzGwdERRVHo3Wsoly6F2aaZM3ci9+9FMnLklOfKliwk3p/3w6loESwJCUROnorp95u2Yu++vXH288UcFwfA/SHD0Xl7kWXYEBRFwXT7Dg+Cp4OdthcpoykETR5I8VJFMRgMjB0wmesRv6eoky1HVr7euJCP67TBoDfg6u7KpPmj8c7qTXxcPCN6j+P+3Qd2zzV40gCK+RQhwZDA+IHB3EiVK2v2LHy5YQEt6rVLsd0oUDQ/X29aSMMyH6R4/b90+uwFZixYxtdzg//T91UUhdbjO/NmyYIYDQl8NXgBfyTbrr3V8T0qvl8NgN/2nOCnWf8DYMaRxdyJsO4zrpy4yLrgVXbLM2HacEombjsC+47iavh1W3mLNs1o1fZjjCYjc6YtZteO/YyaGIiPbwkAcuXOSVRUFEEDxjFq4mDbdOXK+9G5dV/27Tr0r3ONnTqUEqWKYzAYCOo3LkWuT1t/SIu2zTAaTcybsZQ9Ow7wRv7/s3fW4VEcbxz/3EUucsGttEixhRhJIMEhuGtxh+CuQRLcoVCcIoUCRdpCW4oF16IJwckiEYq2aDyX5PL7447LBYLfkfzKfJ4nD+y+O7vfm53ZeWf2ndn8zFkyGQUK7t65j9+wqcTHxQOQI2c2ft39I/WrtDZbmVMoFEycPZqSTsXRaBLxGzqF22GvtqU/71pNo6ptM6zsm5rPbTlP4fhnME2a1MXGRoW3d3O8vNyZNWscrVr1ACBv3tz069eNihUbYWOj4uDBrRw4cAylUsGyZbPw9HTn9993G861dOks2rXrQ2hoBN26taVgwS/TOBsfQ7m65bFSWTGq+QhKuEt0H+fD9B5TdToL5qVac29GNhlOSkoKM1BeZhEAACAASURBVLbO4lTASSJCwukxsRfuVd0JuxpmEh3pUbN+NVQqazo07IFrGWdGThrMwC4jDfZK3uUY6t+fnLlzpEk3euowKnmXI+TKDbPo8qxbDmuVFX7NR1HcvQSd/bszu+d0APIUyEuVZtUY23QkKSkpTN4ygzN7TlG+fkUu/XWRXau3k7/IlwxeNJxRDYeZRE9mLmsNGtXCRmVNg9ptKVO2NJOmjqZz+34A5MmTi569O1Hb+xtUNip2BGzkyKG/cC3tyO9bdjDGd6rhPIUKf0XL1k2oW6MVKSkp7AjYyK4d+7l65cM7T42b1MFGpaJG9RZ4erozY6Y/bVr3BPT51rcrlSs3wcZGxf79v3LwwHHGjx/O98vXsWP7XmrVqsqkyb60b9cHgO4+7XFyKsnxY6c/WNPL2FSpjMLamsd9BmDlVIosA/rxdIy/wW4lFefxsJGkPI807HMYPZLYbduJ33cA20YNULdtRfTan0ym6QXV61fF2saaLo164+LhxLCJAxnadbTBXsHbi0F+fclhVD9bdGjCtYsyK+atoXGbBvQY0oU54xaYVJd3vSqoVNb4NOmHs4cjQyb0Z0S3sQZ7+WqeDPDrTY7c2dOks1fbMWR8fzSaRJPqeR9Wb/iV7QEHsbVRffJre9TxwkplzbQWYyniXpy2/l1Y2FPXgc1dIC/lm1ZhSrMxkJLCmF+nErTnDJq4BCIuh7GgxwyT66nbsAYqlYrmdTviXtaVcVNG0qPjIJ2ePDnp1qsDjWq0QaVSsXX3Oo4dPsmksbrOkqWlJVt3r2XU4EnI127Qpkl3ABo2rcPDB/9+sNMPULtBdVQqa1rV74pbGRfGTB5Kn066Z3muPDnp0rMtzWp1xFql4pedP/DX4VOMnjiEjT9uYfvWAFp3bIZP3w4smfcDVapXYOS4ga+0YaamdgNvVDbWtGnQndJlnBk9aSj9Og832CtXL8+IcQPJZWYdAvPynwz10VNWkqQzkiTFS5J0WZKkai8ML4cCSZLkrd+n1m+HS5LUR5KkY/r0FyRJcpYkaaIkSY8kSfpXkiRfU4isWNGTvXsPA3DmTDAeHq6pP6CsGydPBqLRaIiMjOLWrXBcXEpiY2PDhg1bmTVrkeHY4sWL8OTJMwYO9GHfvl/Inj2byZx+AEdPJ4IPnwPgerBMMdfiBtuje4+Y2GkCWq2WlJQULCwtSdSPBIQEXeN7v6Um05EeHuVKc/zQKQAuBl3GqXTJNHatNgWfVgN4/vR5mv3nz15kyijzjZaV8nQk+EgwADeCr1PUtZjB9vj+I6Z1nmjIM0tLCxITEtnxw5/s37AHAKWFksQE0zkXmbmslStfhgMHjgEQFHgBN3dng829jCtnTgej0SQSFRlNWOhtHJ1LUtrNGdfSTmzbuZ4f1i4gb97c3L3zgDYteqTmq5Ul8fEfN4pdsYIn+/YdAeDs2WA8PFwMtjJlS3PyVFBqvoVG4OxSkjFjphKw+yAAFpYWJOg1eHl54OXlzuofNn6UppexcnUh4fQZABKvXMOqZIlUo0KBxVdfks13ODmXLsK2YX0ALAsXIuGUrvOReOkyVq4ur5zXFLh7uXLioK5+Xjp3Bcd06mef1oOJfJbaKdm48hdWzV8LwBdf5uXxv09Nrqu0lwsnDut+/+VzVynlKqXVlZJC/zbD0ugCGDtnJEtnrjCMxGYEBfJ/wfzp/m8/0AwU9yzFJf1zLTT4BoVdihpsT+4/Yl6XqaQY2gILEhM0FHYpQvZ8OfDdNImha/zIVyS/yfR4lvfg8MHjAAQHXsTVzdFgc/NwIfDFsyMqmvDQ25R0Sq0bXXu15+ihk8jXUgd/bO1sGTa6HxNGf1wnpWx5N44ePAHA+aBLuBjpKu3hRNCZC2g0iURHRRMR9jeSU3GKSUU4sl/X2Qg6fYGy5dwB0Gq1dP6mL8+fRr56IRNSppwbxw6cBOBC0GVc3EqlsWu1KXT9ph/PzKzjU6Mlxax/mY3/suPfF5gIuAChwEZJkt5ntdZpwFzADUgGjgKFgcrAPGCmJEnFXpv6HcmSRc3z51GGba02GQsLi3Rt0dExZM2ahWfPnrN//7E058mVKwfly5dh+fL11K/fnurVK1G9eqWPlWfATm1LTFRMqs7kZJQWuuKTnJRMlP5B0NWvO6FXbnEvTBdmcHz7MVLMXO7tHeyJiow20qY15CHAyaNn0n1gBmzbT4oZxdmq7YhNk2fal/JMd287+XUl7Eoo98PuERsZgyZBQ7bc2Ri0YBgbZ60zmZ7MXNYcsqiJep56D5OTU7U5OKiJjEyrLUsWNTeuhzJrxkKaNuzErh37mTHHn6SkJJ480TmJE6f6cuniVUJvhX+0NuPrG2vL4qAm0jjfoqLJmsWBx4+fkpSURPHiRZg+3Y/p0xeQL19u/PyGMHTIuI/Skx5KeztSYlLLGlot6MuawsaG2K2/83TydJ4M98WueVMsixYh6eYtbCrp7puqckWUNjYm1wW6+hltVA+M8w/g9NGz6dZPrVbL8i0LaevzDcf1zoipdcVEGtVPbdrnxpmjga/o6jm8G8f3n+TG1Vsm1/M+1K5eGUvLjHlhb6u2JS4q1rD98nMtWv9cazO2M7evhvEw7D7P/nnGjqW/MbvdBHYs2Uqv7wabTI/6ped/stF9VDuo09hiomPIksUBACsrSzp0acmKRT+mOV/bjs3ZuW0vT598XGjZy7q0RuX+VV2xODiouXZZplY93RhlzXpVsbWzBeCvI6d59tLAlTlQO9gTFWX8HE5bJ058Ih0C8/JfdvxnybK8S5blG8BsID+Q+z3Sb5Zl+Q9ZlkOAzYAa6Kvfno1uInipN53gXYiMjMbBQW3YViiUJCcnG9nsDTa12p5nz9LvaT9+/JRbt8IJCblBUlISe/cext3ddCN4sdFx2KptU3UqlWiTUyPjrFRWDFs4Alu1Lcv9lpnsuu9CTFQM9mq7NNpe5GFGEhcdi629cZ4pXsmzwQuHYWtvyyr/5Yb9BaVCjN84hY2z13P19BWT6cnMZS0qMhq10fWVRvcwKioatTqttufPozh29BTHj+pGbHft2Iezq25ETaWy5vtV36JW2+M7bNJH6TJoU6evLTIqrW61g5pn+nCaqlUrsPnnFfToMZQbN0Jp3rwhOXNl57fff2TY8L60btOEjh1bfrQ+AG1MLAq71DqAQgn6spaSkEDML1shIYGUuDg0QcFYFStK5OKlqCpXJPvcWaBNQfvcPA16TFQMdkb1U/ke9bN3y0F0b9qPb3+YZnZdCoXirbrqt6hN03YN+X7LAnLmzsGiTXNNriuzExcdh43969sCS5UVvRcMwUZtyzr/lQCEX7xJ8L6zANwIDCFbPtOFikRHxby2fkZHRadpG+zV9kTq62flahU4czIojaML0KxVQzat32oSXfZGuhRv1GVHVGQU08d/R8161Vjz82JSUlJ4+sT0b7rertm4rr69TvwXSDHzX2bjv+z4Gw/JvOi626Z34Gu4afT/WOCBLMtxALIsJwOJwEcHWJ48GUi9etUB8PJy58qVEIMtMPA8lSp5oVKpyJLFgZIli3HlNbHKYWG3UavtKFKkEACVKnlxVT/p0RRcC7xKmeplASjhLhEREp7G7rdqHOHXwlg2Zgla7aedKhN85iJVa1YEwLWMMzeu3XxLik9DSOA1PKqXAaC4ewluyxFp7L4r/Qi/Gs6KscsMefZV8QIMW+rLgkFzOa8PrTIVmbmsnTl9jlq1dZPDy5QtzTWj8wUHXaR8hTKoVNY4ZFFTQipKyNXrzF80lcZN6wJQpVoFLp7XdZLWb1rKlcsyI4ZMMElZPHkykLp1dfnm6emeJl+CAi9QqaKnId8kqRhXr1ynatUKzJkznmZNuxB87hIAy5b9SOVKjalfry3z5i7jl5//5Keftny0PtCF6qjKlwPAyqkUiaGpoVeWBb4i57JFoFSChQXWrs4kXr+ByrMs0WvW8nT4KEjRknA2yCRaXub82UtUrlkBABcPJ26GvH20vPvATjRsqbu3cbHxaM3gfFw4e5lKNcoD4OzhyK2Qt4ertajUnj4tB9On5WAe//uEge2GvzXNf40bgSG4VvcAoIh7ce689FwbvHI0f18LZ+3Y5aTo61/TIa2p01034b5AqUI8uffIZHoCTwdTvZZukQT3sq6EXE0N2zl/7hJeL54dDmqKlSiCrG8fKnuX59D+42nO5eCgxtramvt3H360rqDT5/GupXuj5lbGhetXU9ulC+eu4FneHWuVNWoHNUVLfI187RaVq5Vj4ZwVdGszAK1Wy/HDppsH9E6az1ygml5z6TLOXM8kbanAtPyXJ/em11K8LtQnvXx4ObjaLN7stm0B1KxZhUOHfkOhUNCr1wgGDerBrVsR7Ny5j6VL13DgwBaUSiUTJsx57copiYmJ9Onjy9q1i1AoFJw6FURAwEGT6TwVcBK3Ku7M+m0OKBQsHDGfJj2a8SDiHkqlBU7lnLG0tsLDW9c5WD9rLfK5kLec1TTs33WYCtW8+GnHShQKBf6Dp9Cldztuh9/h0J5jbz+BmTgTcArXym5M/W0WCgUsGbGQRj2a8CD8AUoLJY7lnLCytsTdW9eIbpy9nmZ9v8FKZUW3CbpJt7FRsYYJwR9LZi5rO7fvo1r1SuzcuwmFQsGgfmPp078rYaG32bP7ICuXr2d7wEaUSgXTp3xHQoKGKRPnsmDxdLr5tCM2No6hA/1p0KgWFSp5YW1tTU29MzB10jwCz57/YG1//rmHGjWrcODgVt3qVr1HMnCgD7dCI9i1cz9Ll/3Ivv2/oFQqmTRRl2+zZ4/HytqaFSt1I8I3boQyaODYt1zpw4k/egxrzzI6B1+h4Nn0Wdi3aUXSnbsk/HWCuL37ybl8CSQlERewl6SwcBS2tmQd4wuaRJLCw3k+d75ZtB3cdYTyVT35cfv3KBQKJgyZRsfebfg77C5H9h5PN80fm3YweaE/zdo3Qqm0YMIQ09QBYw7vPkq5qmX54U/dHKTJw2bSvldr7oTf5ejeD5/U+V/n3J7TOFVxxW/rNFAo+GHkEur4NOafiPsoLZRI5RyxtLbExVsXn75l9gZ2Lv2dXvMH41qjDNqkZFaNWGwyPQE7DlDFuwK/BaxHoVAwYsA4evTrTETobfYFHGbNig1s2bkWpVLJnGkLSdDPPytSrDBbN/+Z5lxfFyvEndv30rvMe7N35yEqe5fn111rQKFg1MCJdO/bgYiwvzkQcJS1Kzfz844fUCqVzJ22BE2ChtCbEcxaOAFNgoYbcigTfGeaRMu7sm/nISpVK8fmnT+gUCgYM2gS3froNB/cc/STavmUfG6r+ijMGeOcUUiSlAI0lmV5h37bGbgEfC3LcrgkSQlAd1mWN+jtvYDlgIMsy9GSJIUD38qyvFhvHwCMkGW5sNE14oGOsiy/dcjOxqZgpszkunlc335QBnEz4d+MlvBaHG3yZbSEdNn+MDijJbwWB9X7vGz7tMQmmn4JS1Nw0+PrjJbwRhrczJxL+VkqLd5+UAZx4uKPGS3htfQqO/LtB2UAByJN9+ba1FgprTJaQrpYKDJvMMf1fwPfZ67lJ2FE4XZm9dG+Dd+UqX7zf3nE/02cBYZLknQO+AIYkcF6BAKBQCAQCASfmMy48o45ybzdQvMyAEgCzqNbuWf0mw8XCAQCgUAgEAj+v/lPjvjLsqx4afsyRvH9siyfB7xeSmZsL/xS+sXA4pf2mWftO4FAIBAIBALBJ+HzGu//fEf8BQKBQCAQCASCz4r/5Ii/QCAQCAQCgUDwNj63VX3EiL9AIBAIBAKBQPAZIEb8BQKBQCAQCASfJSmfWZS/GPEXCAQCgUAgEAg+A8SIv0AgEAgEAoHgs0TE+AsEAoFAIBAIBIL/HGLEXyAQCAQCgUDwWfK5fblXOP4CgUAgEAgEgs+Sz8vtF47/J6Fo1i8yWkK62Cky7+2/+fxeRkt4LSVUeTJaQroka5MzWsJriUyIzWgJr0WB4u0HZQCO5//mqluBjJbxWtQWmfPj5XZK64yW8H/JisA5GS3htdR265XREtIll4VdRktIFxvh2gnegCgdAoFAkElxPP93RktIF+eshTJawv8lvcqOzGgJ6ZKZnX6BwNx8bqE+YnKvQCAQCAQCgUDwGSBG/AUCgUAgEAgEnyViOU+BQCAQCAQCgUDwn0OM+AsEAoFAIBAIPktSRIy/QCAQCAQCgUAg+K8hRvwFAoFAIBAIBJ8lIsZfIBAIBAKBQCAQ/OcQI/4CgUAgEAgEgs8SEeMvEAgEAoFAIBAI/nOYxfGXJOlHSZK2fET6LZIk/WhCSSZDkqQBkiSFZ7QOgUAgEAgEAsHHoTXzX2bDXKE+gwGFmc79n0WhUDBuli+SU3E0CRomDJvO7fA7aY7JnjMbG3asopl3ezQJGsP+mvWrUbdJTXz7jjebtu5Te1PQsTBJCUmsGLWYhxEPDPb6Po2p2LgKAOcPBbF1wc+obFUMWDgMdTYHEmLjWTJ0PlFPIk2mZ9HCabi4OJKg0dC3jy+3QsMN9u7d29HDpyNJSUnMnLmQXbsPGGwDB/iQN29u/MfNBKB166YMHOBDcnIyly9fY+AgP1JSPv7Vn0KhoPe0vhQu9TWJmkSW+C7iQcR9g72xT1MqN9HlWdChQH6Zv9lgK1e3PBUbVua7Qd9+tIbFi2bg6upIQkICvfuM5NatcIPdp3t7evbsSFJSMtNnLGDXrv3kzJmd9euWYGtrw737D+nRYyhxcfHUrVudcf7DAAgOvsTAQWOxs7Nl/fol5MiejZiYWLp2G8SjR08+SKep7ucLli6ZyZMnz17Z/yHaFi6chotLKTQaDX36+BIaGpFGm49PB722Rew20jZAr22cXkP79i0YOrQ3kZFRrF//Kz/++PNHaTPWOHf+JJycdRoH9R9LmJHGzl3b0LV7W5KSkpk7ewl7Ag4ZbBUrebLih3k4l6xiEi3paRs2YzDFHIuSmKBh1si53A2/l+aYbDmysvTPhXSt2QNNQiJKpZIBE/pSsnQJrKytWDNvHSf2nzK5roHT+vO1YxESNYnM953PvfD7aY7JmiMr3/0xl961+5KYkIidgx1jl4zGxs6GJE0SswbP4em/T02q64W2TlN7UqBUYZI0iawZtYx/jJ63dXwa4dW4EgCXDp1j24JfAZh3agUP9b/h1rnrbJm9weTa3sbFKyHMW7aaHxfP/qTXVSgUDJ0+iKKORUnUJDInnXKWNUdWlmxbQPdaPdEkJNK+f1u8vD0BUGexJ0fuHLTwaG0WbT2n9qGQ49ckJSSybNTiNG1BI58mVNK3n+cOBfHrgs3YOdgxaP4wbNV2WFpbsnbKD1w/J5tFW9epvfRteyKrRi1N07bX82lE+caVAbhw6By/L/gFla2KfguHos6mJiE2gWVDF5isbReYH7OM+Muy/FyW5WfmOPd/mZr1q6FSWdOhYQ++m7aUkZMGp7FX8i7Hyp8XkjN3jjT7R08dxhC/fiiU5ovcKlu3HFYqayY0H82mWevo6N/NYMtTIC+Vm1VjfIvRjG8+CpeqbhQsWYga7eoQdukWk1qN5cT24zQf2Mpkepo0qYvKxoZq3s3w95/BrFnjDLa8eXPTv193vKs3p1HjjkyZMhpra2tsbGxYs2YBffp0MRxrY2PDpIkjqVO3Nd7Vm5MlaxYaNqhlEo3l6pbHSmXN6OYjWT9zLd3GdU/VWDAvVZtXY0xzX0Y3G4lbVXcKlSwMgM/EnnQc1QWF8uP7zk2b1sPGRkWVqk3w85vB7NmpHcO8eXMzYEB3qlZrRoOG7Zk2VZdP/n5D2bz5D6rXaMH585fp1bMTarU9M2f607RZZypXaUx4xN/kypWDHj4dOHfuItVrtOCXX/5k7JjBb1Dzekx1P1/Qo0cHnJxKfpCW9LTZ2Kjw9m6Ov//MV7T169eN6tVb0LhxJ6ZMGaXXpmLNmvn06dPZcGzOnNmZOHEEdeq0platVrRt24xChb4yicaGjWujUqmoW7MVk8bPYer0MQZbnjy56N23M/VqtaFls66MnzQCa2trAL788gv6D/TB0sp8072q1KuESmVN3yYD+X7GKvqP75PG7lWtLHM3zSJHruyGfXVb1sbSyoJ+zQYzpvt4viyc3+S6KtatgJWNNUObDWP1jDX0Gtczjb1MNQ+mb5hGNiNddVrVJiwknBEtfTmy/Sit+nxjcl0AHnW8sFJZM63FWH6d9RNt/VPLeO4CeSnftArTWvgxrflYnKq48VXJQuQplI+Iy2HMajuBWW0nZIjTv3rDr0yYuSDNoNSnonK9SlirrOnfdBArZqyi77i05cyzWlm+3TiL7Eb3c+OSzQxpNZwhrYbz7/1HzBhqns6Kl74t8Gvuy0+z1tHFP7UtyFMgL1WaVcOvxSjGNveltL4taNSjKZf+usiENmNZMnwBPab0ecMVPpwydb2wUlkxqfkYNs/6ifb+XQ223AXyUrFZVSa1GMuk5mNwqepGgZKF8G5Xm7BLt5jSyp+T24/TbGBLs2j7VGhTUsz6l9l4r6e9JEnngc2yLM/Uby8BugJZZVlOkiQpH3AfWA/YybLcUpKkrsAA4BdgKGAFBAC9ZVmO0Z+nEzAB+ALYCFgbXdMB+B6oB9gAx4CBsizfkCTJG/gDGALMAGz11xksy3KcPn0JYCFQFfgX+BkYJ8tywjvaPYFFgCtwFjjxPnn2PniUK83xQ7pRrYtBl3EqndZx0WpT8Gk1gF/3rk2z//zZixzcfYRWnZubSxqSZykuHDkHwM3g6xRxLWawPb7/iJmdJ5Gi1b3UsrC0QJOQyO7V2w2dkVz5c/H80XOT6alU0Yu9ew8DcOZMMB4ergabZ1k3Tp48i0ajQaPRcOtWOC4upbh1K5wNG7Zy8OBxpBJFAUhISKCadzPi4uIBsLSwID4hwSQaS3k6Enw4CIDrwTJFXYsbbI/uPWJyp4lo9XlmaWlJor6xDAm6xuk9p6jTod5Ha6hU0Ys9e3Wju6fPnKOMcT55unHiRKAhn27eCsfVpRSVKnkxc9YiAPbsOcSUyaO5eu06ly+HMGf2BL7+uiCr12zi0aMnLFy0CqX+HhcomJ+H/zz6YJ2muJ8A5cp54OXlwaofNqTZ/6FUrOj5Wm1ly7px8mTgS9pKcutWhEFbiRK6uvL11wW5cOEqT5/q6kFg4EW8vNyJiLjzyjXfl/IVynJg/1Hdec+ex83D2WArU7Y0p08FGTSGhkbg5Cxx9YrMvAWTGTLQn0PH//hoDa/D1cuF04fOAnD13DVKukpp7FptCkPb+rJq9zLDPq9qZQkNCWP2ummAgvnjFplcl5OXE4H6+hkSHEJxo/oJkKJNYXS7MSzelXrtsJBwChQrAICdgx1Jickm1wVQ3LMUl44EAxAafIPCLqnl+Mn9R8zrMjXN8zYxQUNhlyJkz5cD302TSIzXsGnKGh6E3kv3/OaiQP4vmD/dnzGT53zS6wK4eDpz5nBqOZNKl0hj12q1DG/ry4rdS19JW6V+ZaKeR3H2SKBZtJX0LMV5fft5I1h+pf2c2jm1LdC1nxp2/PAnSQmJACgtlIb2wdRInqW4qC9rt4Kv87Vr2rI2u/OUV8rantU7DG17ThO37QLz875DxLuBGkbb1dE52x767TpACK+GNbkCVYBaQE+gBdAHQJKk6sBqYJ7+PNFAY6O0U4EigLfertUf/wI1MBL4Bmiiv8ZS/bltgD1AKOAOdELXgVjwjvYc6DopF/TXXoeu82IW7B3siYqMNmxrk7VYWFgYtk8ePcPzp6++TgvYtt8koSlvwlZtR2xUbBptSgtd8UlOSibqaRQAHfy6En4ljAdhugYnRavFf9Nk6nZtyPlDQSbT45BFTeTz1LxI1iYb8sohi5rnz6MMtqjoaLJmdeDZs+fs1ztHL0hJSeEfvbPar29X1Gr7V475UN6eZzr9Xfy6E3ollHv6PPtr+3GT3c8sL+VFslGZypLFgeeRqXkYHRVDlqxZcHBITRMVpcu7XLly4F2tImPGTqNR444MGtiD4sWL6H6XVsvePb/Qv193AgIOfpBOU93PfPnyMM5/GIMH+32QjvR4OQ+1RtpetkVHx5A1axa9tmNpznPzZjiOjiXIkycXtrY2VK9eCXt7O5NodHBQE2ms0eg+v2yLjoohSxYHZs+dyKKFP3D//kOTaHgd9mo7oqNiUrVpk7GwSG16Ao8FEfnScy1rjqx89fWX+Hb2Y+PSzYyZ52tyXXZqO2IijXQZ1U+Ac8eCiXoWlSZN1NNIPKp6sOLAclr2/oaAzXtMrgvAVm1L3BueHdH6522bsZ25fTWMh2H3efbPM3Ys/Y3Z7SawY8lWen33YW/fPoba1StjaZkxiwXaO7xUzpK1acpZ0LFzRD5LPxylQ/92rP1uvdm06dqC9MuacfvZ2a8bYVdCuR92j9jIGDQJGrLlzsbgBcPZMGudGbW9vay18+tCxJUwHoTpQpRStFrGbJpEna4NTNq2ZwQpZv7LbLxvDd0NDJIkyRrIDhQGDqBz6s8AdYFdQM6X0lkBPWVZfgBckSQpACijt/UB/pBl+YWzPgyd8/2CwkAUECbLcrQkST30+15gAfSRZfmEUfpfJEkajK4zkAj0l2U5BZAlSeoDHJMkyfcd7G2ABGCALMuJQIj+DcDHD8WmQ0xUDPbqVEdAoVSSnGyeEaX3JS46Flt7W8O2QqlAm5zav7NSWdF7zkDiouNY7b88Tdqp7caTv+iX+K4Zx5CqpnldGRUZjdpBbdhWKlLzKioyGgcjm4NazbPXPPBBF+M4Y7ofxYsXoU3bXibRB7o8s1G/Oc8GzBlMXEwcK/yWpXeKjybypbxQGpWpyMgoHNSpNrWDPc+fPScqKhoHB3vi4+NxcNDl3ePHTwkMusDDh/8CcOzYKUqXduLGjVAA6tRtjSQVZdsf6yhZqtJ76zTV/fymRUNy5szOtm3ryJc3N7Z2tsjXb7F+t0OgRgAAIABJREFU/a/vrekFL+ehQmGch7q8eoFabf9abc+ePWfkyMls3rycO3fuc/785Q+aD5EeUVFp88/42fGyTe1gT2JiIhUqlqVI0UKMGjOQ7Nmz8sOP8/HpOsQkeoyJiY7FLk09UJKc/OYpb5FPIw0x/edPXaRAEdOERBkTm44u7Vt0dRjagV+X/cquDbv5umRhxq3wp2+dfibXFhcdh43967VZqqzwmd2f+Jg41vmvBCD84k1Dvt4IDCFbvrQhof91YqJisTPqSCuVireWM4BCxQsSHRn9ynwAUxIXHZvmfirTaQv6zRlEXHQcq/y/N+wvKBVi6OKRrJu2mqunr5hNm20abcpXtPWc05/46HjW+K9Ik3ZGuwl8UfRLRqzxY3hV09cDgXl43xH/E+gc5fLoRvtPAweBKpIkKYDawM500kXpnf4XRKLrDAA4A+deGPQO+FmjY2eg6yT8q+8wNEE3Av+CZOCk0fYZ/bklwAnd24IoSZKiJUmKBvah+93F38HuDFzSO/3G5zcLwWcuUrVmRQBcyzhz49pNc13qvbkeGIJbdV1frZh7Cf6WI9LYh68cS8TVMH4Yu8zwWrBpv2+o3NwbgPjYBLQm7MScOHmWevV0L5+8vNy5ciXEYDsbeJ5KlbxQqVRkyeJAyZLFuHLl9ZOiliyZiY2NipatfAwhP6bgWuA1ylQvC0AJd4nbIWnzbMwqf8KvhfH9mCWG17ym5sTJs9TX51M5Lw8uX75msJ09e57KlY3zqTiXr8icOHGW+vVqAlC3bnWO/3Wac+cu4uQokTNndiwsLChXzoNr167j6zuADh10cc4xMXEkf+DvMNX9XLJ0DRUqNqROndbM+XYpP2/+46OcfoCTJwOpV696utoC30ObLt/cqVmzJT4+Q5Gkopw8aZrQgtOngqhdpxoAZT3duGakISjwAhUqlkWlsiZLFjUlpKIEBV7Ay6MOjet3oHH9Djx9+twsTj/ApbOXqVCjHACOHqUIvRb21jQXz1yivD5NUcciPLz7j8l1XT17Fc8auomdJd1LEh7ydl3Rz6OJ0Y+OPnv8HDu1ad7YvMyNwBBcq+tepBdxL86dl563g1eO5u9r4awduzz1eTukNXW6NwSgQKlCPLn3YWF3/69cDrxC+RpegL6cvcP9BChTxYPTh8zWrAMQEngND31bUNxd4vZL93PUSj8iroaxYuxSQ1vwVfECDF86ivmDviX48LlXzmkqrgeGUFpf1oqm07YPXTma21cjWD32e0NZa9yvBZWa6543CbHxb+0wZ3a0pJj1L7PxXiP++jj+/ejCffIBR/R/w9GFwryIwe/8UtL0gtMUr/n/i+Mt9dc8JUlSYXThP/WB6UA/SZK89Me+vGLSi85Msv4cJ4FuvMrdd7C/TptZ2L/rMBWqefHTjpUoFAr8B0+hS+923A6/w6E9x95+AjNyNuAULpVLM+m3maCA5SMW0aBHEx6G30dhoaRUOSesrK1w89Z1DjbPXs/hX/bTd+5gqrephdJCyfcjTRenu21bALVqVuHwod9RKBT06jWcwYN6cutWODt27mPJ0tUcPLAVpVLB+AmzSXhN3L6bmzPdurbl+F9n2LNHt8LK4sWr+fPPgI/WeDrgJG5V3Jjx22zdqjUjFtCkR1PuR9xHqVTiVM4ZK2srPPR59tOstcgmXrXhjz92U6tmVY4e2YZCoaBHz6EMGdyLm7fC2LFjH4sXr+bwod9QKpWMHz+LhIQEps9YwOof5uPj055Hj5/QqVN/YmPj8B83g107NwLw65btXLki8++/j1n9w3y6dW2LhYUFPXt8WCScqe6nOdi2LYCaNatw6NBvem0jGDSoB7duRbBz5z6WLl3DgQNbUCqVTJgw57XakpOT0WgSOXlyJ/HxCSxYsJLHj02zIsyOP/dSvUYl9uz/BRQKBvQdRb8B3QkLjWD3rgMsX7aOXXs3o1QqmTppHgmfcPLl0d3HKVu1DEu3LdS9XRs6mza9WnIn7C5/7TuZbprtG3cxfMZgvt++CAUK5o6eb3JdfwWcwKOKO9/9PhcUCuYNn0eLns25F36PU/tOp5tm7bfrGDp7CI07N8TC0pIFoxaYXBfAuT2ncariit/WaaBQ8MPIJdTxacw/EfdRWiiRyjliaW2Ji7c7AFtmb2Dn0t/pNX8wrjXKoE1KZtWIxWbRllk5tvs4Zat4sPiPBSgUCmYNm0Ornt9wN/weJ15TzgAKFClA0DHzhqqcCThF6cpuTPttFigULBmxgEY9mvIgXHc/HfVtgbu+Ldgwex3N+7bESmVF9wm6SeexUbHM6jnN5NoCA07jXLk043+bjkKhYMWIxdTv0ZiH4Q9QWigpqW/bS+vL2s+zf+LILwfoM3cQ3m1qorRQsmLk51XW/t9RvG8ssSRJPugm9GYDBgHHgWfANkAly/I3+jX41UaTe7+VZTmX0TmM7ZvQTQRuamQ/B1yUZbmrJEljgLOyLO/X20oAMlAOsAMOAaVlWb6ot7cANgC5gI7AJKCwLMvxersX4Av4AG3fwT4ZKGg02Xce0EKW5cLvmmdOectlvi4f4GrzRUZLeC2/Pcy8MYMN8rhltIR02fHAfKNCH4vSjCtOfSyKTLrysK2l9dsPyiCcsxbKaAmvxU6ZefMtv4V53hB8LCsCP/1k3PehtpvpQjBNSa5Mej9tzLZS+8fzU8Rvme6B265QM7P6aJsi/shUv/lDWuPd6JzuYsApfRjMCXRO8q4PON8CoIEkScMkSSohSdJUoLSR/StgoSRJFSVJ+hpdpyMSuG50zEpJkjz0q/zMBVbpVwz6Cd3I/1pJkpwlSaoArAHsZVl+/g72zXr7SkmSSkqS1B7o8QG/USAQCAQCgUCQyfjcPuD13o6/LMv3gGtA4IslM9GF+yjQdQre93yngFboVvu5ADgCm4wO8QX+An7XX7cG0OCl7wRsRrc6zxZgKzBMf+4YdCsNZUcXm78d3fyB9u9of45ulaBCQDAwAvjufX+jQCAQCAQCgUCQ0bx3qE9mQj/CfwhwkGU5+i2HZxgi1Of9EaE+748I9fkwRKjP+yNCfT4MEerzYYhQn/dDhPq8H60KNTWrj/ZrxLZM9Zszb2ssEAgEAoFAIBAITEbm7RYKBAKBQCAQCARmJCUTLrlpTv6vHX9Zlg/z6nKbAoFAIBAIBAKB4CX+rx1/gUAgEAgEAoHgQ8mMK++YExHjLxAIBAKBQCAQfAaIEX+BQCAQCAQCwWdJZlrdUpIkS3Tfo2qPzkdfD4yQZfmVz61LkpQdmA00AqzRrXI5WJblu2+6hhjxFwgEAoFAIBAIMp7pQH2gCdBM/+/01xy7BnADmgLegAPwpyRJFm+6gBjxFwgEAoFAIBB8lmgzyao+kiTZAP2AdrIsn9TvGwhskiRpnNFHc5EkKRc6h7+8LMtn9Pu6A3cAZ3QfxE0XMeIvEAgEAoFAIBBkLG6APXDUaN8R/b6XvxwaBzQAzhvtezFP2fZNFxEj/p+A6raFM1rCa7mrjc1oCenSPp9XRkt4Lefi72e0hHRxzFGIe3GPM1pGulgoMu8Yw95sxTJawmv5jsz5FdoxtjEZLeENZI7Ru/So9fB6Rkv4v2Tf+RUZLSFdNpQen9ES0qVFg38yWsL/FZloVZ8vgRhZlp+/2CHLcqQkSbHAV8YHyrIcA+x+Kf0wIBK4+KaLCMf/MyazOv2CDyOzOv0CgUDH/rxfUuvhG+fdZQi13XpltITXklmdfoAOFyZnWudf8H+JHZCQzv4EQPWmhJIktUXn+A+QZfmNzp1w/AUCgUAgEAgEnyWZ6Mu9caTv4KuA1zrzkiR1BVYB82VZXva2i2Te9+8CgUAgEAgEAsHnwR3AXpIkhxc7JEnKgu5NQLqvCiVJGopudZ85siwPf5eLCMdfIBAIBAKBQPBZoiXFrH/vwQUgBqhitK+aft8rq/ToV/GZB/jLsjzmXS8iQn0EAoFAIBAIBIIMRJblOEmSVgKLJUnqAiiARcASWZbjJUnKCljLsvyvJEn59LZNwA/67Rc8lWU5vbkCgHD8BQKBQCAQCASfKZnpy73AaHTLcW4HkoANgJ/etgDdh7oKo/tarx3QTv9nTGNgx+suIBx/gUAgEAgEAoEgg9GP1PfR/71s62r0/1XoJvS+N8LxFwgEAoFAIBB8lmSidfw/CWJyr0AgEAgEAoFA8BkgRvwFAoFAIBAIBJ8lmWgd/0+CcPwFAoFAIBAIBJ8l77nk5v89ItTnA5AkKVySpAEZrUMgEAgEAoFAIHhXxIh/JkKhUNBmqg9flipEkiaRDaOW8yjiocFe3acBZRpXBODKofPsXrAFGwdbui0ajMrWhqTEJNYOXUTUv8/Noq33tL4ULvU1iZpElvgu4kHEfYO9sU9TKjfRfXMi6FAgv8zfbLCVq1ueig0r892gb02u64W2TlN7UqBUYZI0iawZtYx/Ih4Y7HV8GuHVuBIAlw6dY9uCXwGYd2oFD8N1v+HWuetsmb3B5Lr8Zo6ghFNxNBoNk4bN4O/wtB/fy54zG2u3L6dl9U5oEjSoHeyZvmQC9g72WFlZ8e2EhVwMumwyPbPnTcTJWUKToGHoIH/CQm8b7B27tKJLt7YkJSUxb84y9u05TLbsWTkVtIeQq9cB2LVjPyu+X2c436ZfV7B71wHWrt6c7jU/VOfMueNxci6JRqNh2MBxhIel6uzQuRWdu7UmKSmZ+d9+z749h7Gzs2XWvAkUKPgl1tZW+PlOI/jcJZNp0gvjy6l9sSn1NSmaRO6MWoTGqA7kn9ALu7Kl0MbEARDecyoWDvZ8NXsQCksLUCi4O2YxCaHpfoDxI6VlzjqgF0ee8QNQlSxCiiaRh+O+I/F2ar7lHtsXWw9HQ77d6z8RbbTu6/S2ZZ3JN3sUYTU6/ed1KRQKpn3rTyl9/fQdPIGIsL8N9nadv6FDl1YkJSex6NsVHNh7lAnTfXF0LqnTmycXkZGRjB02hQnTRxnSuZd1pWenwRw58JfJdA6dPoiijkVJ1CQyZ+Rc7obfS3NM1hxZWbJtAd1r9USTkEj7/m3x8vYEQJ3Fnhy5c9DCo7VJ9LwPF6+EMG/Zan5cPPuTX9uAQkGFGV3J7lgQbUISf41cRVT4w1eOqbVuBH/vDUJef9Dsemw6DET5VRFISiRu7Xek/Jt6Py2dPbFu3BEAbcRN4jcuwrpeGyydy+qS26lRZMlO9Ii25tVpRjLZcp5mRzj+mQjXOp5YqqyY22Ichd2L08K/Eyt66pzlnAXy4Nm0MnOa+UEKDPl1Ehf2nKFEBSfuhfzNtpkbqNi2BrV6NeH3aetNrq1c3fJYqawZ3XwkJdwluo3rzowe0wDIWzAvVZtXY1STEaSkpDBt60xOB5wiIiQcn4k9cavqQdjVUJNreoFHHS+sVNZMazGWIu7FaevfhYU9ZwGQu0BeyjetwpRmYyAlhTG/TiVozxk0cQlEXA5jQY8ZZtNVo35VrG2s6dyoFy4eTgyfOIghXVMb5Ire5Rjs15ecuXMY9nXq05bTxwLZsPIXChUtyKxlk2hbp5tJ9DRoVAsblTUNarelTNnSTJo6ms7t+wGQJ08uevbuRG3vb1DZqNgRsJEjh/7CtbQjv2/ZwRjfqa+cb+y4IWTLntUk2oyp36gWNjYqGtVph0fZ0kyc5kvX9roXbLnz5KJH747Urd4SlY2KP3dv4Mihv+g3qDshV28wsM9oSjmVwMm5pMkd/yx1yqNQWXOrxUjs3CW+8O9ORM9pBrutc1HCOk8g+WmkYV/+ib14vG4nkXtPoa7qTj7fzkT0MX2Zy6x1AEBdqyIKlTV/txuKTemS5Pbtxb0Bkwx2G8di3Onhh/ZZZJp0lvlykb3rN7pO02egq27DGqhUKprX7Yh7WVfGTRlJj46DAMidJyfdenWgUY02qFQqtu5ex7HDJ5k0VufAWlpasnX3WkYNnoR87QZtmnQHoGHTOjx88K/JnH6AyvUqYa2ypn/TQTh6lKLvuD74+4w32D2rlaXXmB5kz5XdsG/jks1sXKIbHJjx41SWT/+gVQg/itUbfmV7wEFsbVSf/NrGFKxXBguVFbuaTCK3R1E8x7fnYPfv0hzjMaoVqmz2n0SPpVtFsLImduYQLIqUxKZ1L+KWTNQZVbaoWvYk9tsRpERHYl23FQp1VjQBP6MJ+BkA24GT0Wz99PdT8OFkesdfkqRC6D5aUAOIAzYDI9B9wGAuUBXdxw5uAGNlWf5Tn645MAUoBtwHlsqyPEdvCwe+lWV5sX67MBAGuMiyfFmSpLzoPoNcB8gK/A3M0K+bajaKekpcO6L7KnN48A0KuhQ12J7ef8ySLjNI0ep6phaWFiQlJHIv5DZ5i+YHwEZtR3JSklm0lfJ0JPhwEADXg2WKuhY32B7de8TkThPRanWLYllaWpKYoAEgJOgap/ecok6HembRBVDcsxSXjgQDEBp8g8JG+fbk/iPmdZlKil6bhaUFiQkaCrsUIXu+HPhumkRivIZNU9bwIPReuuf/UNy9SnPi4GkALp27glPpkmnsWq2WXq0HsWnvGsO+n5b/jEajyztLSwsS9PloCsqVL8OBA8cACAq8gJu7c6rWMq6cOR2MRpOIRpNIWOhtHJ1LUtrNGdfSTmzbuZ5Hj54w1ncqDx/+S+OmddFqUziw76jJ9L3Aq7wHB/cfB+Bc4AVKuxnrdOHM6XOpOsNu4+gk4V2jMtt+382mrSuJjopmzIgpJtdl7+lI1BFdHYgNlrFzSa0DKBRYF/6Cr2b0xzJXNp78vI+nv+7n/tTVJEfF6A6xsECbkGhyXZB56wCArYcTsccDAYi/EIKNc9p8syqUn7yTB2OZMxvPt+4h8re9KKytyDNxEP+MX0DBLYtMrikz6vIs78Hhg7pyHxx4EVc3R4PNzcOFQKP6GR56m5JOJbgYfAWArr3ac/TQSeRrN1J/n50tw0b3o2XDribV6eLpzJnDZwG4eu4aUukSaexarZbhbX1ZsXvpK2mr1K9M1PMozh4JNKmmd6FA/i+YP92fMZPnfPJrG5PXS+LuoYsA/HvuFjldv05jL9TQkxSt1nCMubEo7kzSZd39SA4NwaJQ6v20KOaI9m4Yqla9UebOR+KxAFKiUyMKLN0rkRITTfKVoE+i1VyIGP9MhCRJKmAfOse+KtACaApMRPdVsyigPOAGXALWSJJkrXfcfwa+AyRgJDBNkqSa73jpdUAeoCbgCPwJLHvpk8gmx0ZtR1xUrGFbm6xFaaG7RdqkZGKeRgHQfGxH7lwN55+w+8Q8i6ZkFVf89s2lVu/GnPz5kFm02artiH2NtuSkZKL0o5xd/LoTeiWUe2E6B+Kv7cfN/hrNVm372nxLTkomWp9vbcZ25vbVMB6G3efZP8/YsfQ3ZrebwI4lW+n13WCT67J3sCMqKtqwnZycjIVF6ijhqaNnef407WhiVGQ0CfEacubOwfTFE1g4fZnJ9DhkURP1PH09Dg5qIiOjDLbo6BiyZFFz43oos2YspGnDTuzasZ8Zc/wpWao4LVo1Yua0BSbTlkang5ooIy0v64yKTP0NMdExOGRxIEfO7GTLloV23/Rkb8BhJkz1NbkuC7UdWqNylpKsBX05U9rZ8HjtDm4PmUdYl4nk7NQAm5KFdaP/ScmoinzJF37d+WfBJpPrgsxbBwCUajtD5wfS5pvC1oZnG/7kge9s7vT0J1u7RliX+Jo84/rzdPUWkv55bBZNmVGX2sE+TdlO1moN5V6dTrnPksUBACsrSzp0acmKRT+mOV/bjs3ZuW0vT588M6lOewc7oo3yTZusxcIi1ZUIOnaOyJfekrygQ/92rP3O9G+k34Xa1StjaZnxY51Wals0xs8RrRaFPv+ySV9RpFlFguds/WR6FDZ2EJd6P9FqQamvB+qsWEilSdi6itgFfljXao4y75eGQ1UN2pKw/adPplVgGjK+FryZWkAhoJIsy/8CSJLUG90o/ipgtSzLT/T7vwXaAnmBXIAV8LcsyxFAhCRJD4Hr73jdncAOWZZD9eeeCgwBigMP3pTwY4iPjkVlb2PYVigVaJNTPy1hqbKi4+w+xMfE87O/7uVD/cEt2b98O39t3E/+kgXpsWwYM+qb3umJi47FRm37Wm1WKisGzBlMXEwcK/xM56y+m7Y4bOyNtSlfyTef2f2Jj4ljnf9KAMIv3iRZf8yNwBCy5cuBqYmJisVebWfYViqVJCcnvzVdsZJFmL18MnMnLSbo5HmT6YmKjEbtkPr62FhPVFQ0anWqTa225/nzKIICLxIXq4tx3rVjH6P8BtG6XTO++CIvv29fS4GCX5KYmMjfEXc5qH+b8NE6X9LyJp32ansin0fy9Mkz9uzSxcLu3X2IgUN6mESLMcnRsSiNyhlKBejLkDYugUdrtpMSn0AKEH3iIjalviY+JBz7Ci58OaUvfw+dZ5b4fsi8dQBA+4Z8S4lP4Om6P0iJTwAg9vQFbEoVxbaMM1YF80N/sMjqQL65o3kwfOZ/Wld0VMxry310VHSaZ8mLcg9QuVoFzpwMSjPIANCsVUP6dB1mEm3GxETFYmdv/FxTGMrRmyhUvCDRkdGvzAf43EiMjsNKnbaupujzr2jLytjly069X8aiLpCL5MQkov9+xN3D5hv9T4mPBZuX6oH+7WBKdCTJ4ddJiXwKQNL1SygLFEX78C7KLwqSEhuTZj7A/yuf23KemXrEH91oe8QLpx9AluU9siwvAZYB30iStFySpEPAixkwFsB5YAOwR5KkW5IkLQTiZFl+aQbNa1kGlJckabEkSXuBEKNzm43QQBmn6u4AFHYvzj35dhp775UjuXMtgs1jVxpCfmKfRxOvHz2IevwcGwdbzMG1wGuUqa6bzFPCXeJ2SEQa+5hV/oRfC+P7MUsMIT+fihuBIbhW9wCgiHtx7shptQ1eOZq/r4WzduxyQ7hD0yGtqdO9IQAFShXiyb1HJtcVfPYilWtWAMDFw4kbIbfemqZIicJ8u3Iao/tO5K+Dp0yq58zpc9SqXRWAMmVLc+1qaj84OOgi5SuUQaWyxiGLmhJSUUKuXmf+oqk0bloXgCrVKnDx/BUmj59DvZqtadaoM5s3/s6yJT+azOkHOHv6HDXr6HR6lC1tmFis03mJckY6i5coQsi1G5w5FUTNOtUAKF+pLHLITZPpeUFM4DUc9HXAzl0i3qicqb7OT9Ets3QjZZYW2Hs6Enf5FvYVXMg/vidhXSYQd8n0ml6QWesAQNy5K9hX9QLApnRJNNfDDTbrwl9ScMNcQ77ZejgRf+UG4Q16cKeLL3e6+JL8PMrkTn9m1BV4OpjqtXQLJLiXdSXkamrYzvlzl/B6Ue4d1BQrUQT5mq48VfYuzyF9aNwLHBzUWFtbc//uuzZ5787lwCuUr6HLN0ePUoSGhL1TujJVPDh96IzJ9fy/8c/Z63xVozQAuT2K8vRa6gTuoGmb2dl4IgGtpnHzl2NcXbHbrE4/QPLNK1i66O6nRZGSaO+EG2zaiBtYfFkYhToLKJVYFimF9p7OL7Es5UHS5bNm1SYwD5l9xF8Dr3bFJEmyB06ji/n/HV0oTjRwGECW5RSgo/4tQFOgAdBPkqRusiyvT+echnyQJEkB7AHyo5tPcAC4Asim/GHpcWHPWUpWcWXY1skoFAp+GrmMGj4N+TfiAQoLJcXKlcLS2hInbzcA/py9iZ1zf6H9rN5U6VgHCysLNo1eYRZtpwNO4lbFjRm/zUahULBoxAKa9GjK/Yj7KJVKnMo5Y2VthYd3GQB+mrUW+ZzZswyAc3tO41TFFb+t00Ch4IeRS6jj05h/Iu6jtFAilXPE0toSF29dp2rL7A3sXPo7veYPxrVGGbRJyawasdjkug7uOkKFqp6s3b4chULB+CHT6NS7LbfD7nBk7/F00wwa2xdrG2t8pw4BIDoyJs2E4I9h5/Z9VKteiZ17N6FQKBjUbyx9+nclLPQ2e3YfZOXy9WwP2IhSqWD6lO9ISNAwZeJcFiyeTjefdsTGxjF0oL9JtLyJXdv3U9W7Itv3bEShUDCk/1h69+9CWOht9u4+xKrlP7Ft908olEpmTp1PQoKGBXNXMHfRFHbs3URiUhKD+pgmz4yJ3HMShypuFN06GxQK7oxcQC6fpmgi7hO5/wzP/jhMsd+/JSUpiae/HSThxm0KLlyIwtqKAnOHApAQepe7Y5eYXFtmrQMA0ftPYFfRgwIb54FCwYOxc8nWpQWJt+8Rc+gUkTsOUnDzfFKSkojcth/NzYi3n/Q/qCtgxwGqeFfgt4D1KBQKRgwYR49+nYkIvc2+gMOsWbGBLTvXolQqmTNtoWH+T5Fihdm6+c805/q6WCHu3DbPSOyx3ccpW8WDxX8sQKFQMGvYHFr1/Ia74fc4se/ka9MVKFKAoGP/37HgpiBidyD5qzrTYNt4UCj4a+gKHHvVJyrsIX/vO/fJ9SQF/4Wlowd2o74DhYL4H+diXfsbtP/cJenCKeJ/W43dEN0CAImBR9DeCwdAme8rkq5+er3mQPuZreqjyMzLGEmSVB+dY5/fKKSnMzAQcAWyyrIcr9/f+n/s3Xd4FFXbx/HvbnpIKCIISm8HQu+gdBSlPSqCikhTsVAF6UWQoggiiGJBRQGxgKhY6IrKY6H3cmgJHQUp6XX3/eNskk1IwOd1N7OY++PFJdmZzf6Y3Z05c+Y+ZzB1/RWAgkBvrfUwt9/1MVBYa91BKaWBRVrraa5l7TCN/ZqYk4K9QEW3Up9GmBONNlrrDdkHB1/PwHIP+eRGPu2Iv/5KFilsD7Q6Qq62J569/koWOJPgvXrof8rP5rsXF9cWrmR1hFzNxje/B2NC4q6/ksjRnX94p9zrn6gQeovVEXK1bqd3OrM8ZUnt56+/Uh7r0uFPqyPkquC7a21WZ8iuxW1tvdpG+/n09z71b/b1Hv+1wBHgQ6XUWKAwZmDvISAQeEgp9SNQD5jjek4QcBF4Ril1CfjDBVOkAAAgAElEQVQI03vfBEgfVbQFeEoptdL1e6aSeRXgMpAGPKyUWgJUAea6/W4hhBBCCPEv4JM9s17ku91wgNY6DVOq4w9sBpYBS4FOwDhgOrAfeB4zxecloL7W+iRmBqD7MWU6y4GvgPRJt8dhTh5+w8zgMwlwuF7zNPCk689BTKP/TWA3UN+L/1whhBBCCCG8xtd7/NFaH8XU6Gf3ouuPu4/dnrcKWJXL7zyOmTHInb/b8gXAgmzLX3dbXu56uYUQQgghhG+TefyFEEIIIYQQ/zo+3+MvhBBCCCGEN0iPvxBCCCGEEOJfR3r8hRBCCCFEvuTL09p7g/T4CyGEEEIIkQ9Ij78QQgghhMiXpMZfCCGEEEII8a8jPf5CCCGEECJfcuazHn9p+AshhBBCiHwpvw3ulYZ/HpjW/ILVEXK18vsSVkfIUetKp62OcA2BvHKipNUhrvKdI9XqCLm6mBRtdYQbkr+PVmM6nTarI+TKZvPtg3iAPcDqCFe52S/U6gg3rB67Jlsd4SqOCyetjiB8mDT8hRD52hLCrI6Qo4v47omcEHllSe3nrY6QK19s9Iv/nQzuFUIIIYQQQvzrSI+/EEIIIYTIl/Jbjb/0+AshhBBCCJEPSI+/EEIIIYTIl6TGXwghhBBCCPGvIz3+QgghhBAiX8pvN/CSHn8hhBBCCCHyAenxF0IIIYQQ+ZJDZvURQgghhBBC/NtIj78QQgghhMiXpMbfByilWimlnEqpsL+xbjml1L15kcvtNQcqpaLy8jWFEEIIIYT4J3y1x/9XoCQQ9zfW/QDYBqzwaqK8YLMR3HsIfmUqQmoyCe/NwvHnmYzF/rUaEXR/TwDSog6TuHAu2OwE93gGv/JVwD+ApC8Xkbrzd69ka/BSX4pElCEtOYXNw98jNuqPq9ZpuXgEp9ds48ji7zMeLnVPA0p3bsxvA+Z5PpfrdQsOG4p/xYqQksKVGTNJO306Y3H44EEE1qyBMz4BgEtjx2EvGE6hsWMAG2l//MGVma9AUpKHY9noMvUxSlYrQ1pyKktHzeev45nbrPnj7anb+XYADmzYybrXlhNSqACPzBlAcFgIcZdj+Xz0u8T+Fe3RXOnZnp8xiqrVK5OclMyEYdM4EXkqyzpFihbmk+/e4z8tHyE5KTnj8Ts7tOLuzm0Z8cwEj+Z5+dWJVK9RlaSkZIYNHk/UsRMZyx/t3Y2efR8iLTWV2TPfZt2aHylcpBC/blvNwf2HAVj17Tp+/WULU14am/G8+g1r0+eRAWz4/r8ey5qe9z7Xe5uanMrybO9t0553Ub9rS3A6WT/3Cw7+sMOjr59Tnh5Tn6BUtXKkJqewcNTbnD9+LmP5nY93pGHnOwDYu2E737z2OTa7nQcn9KZczQr4BwbwzZyl7P5huzfCccvEAQRVrYAzOYVz4+eQcuJsxuLi454mpF4Ejjjz/Tzd/wUcsfEAhDSoQcmZIznWupdXchV/fmBGrj8mzM6Sq9jYZ7LkOjNgUpZcJWaMIrJNTw/GsTF55hiqVq9CcnIyY5+dwvHIkxnLH+p5P917P0BqahrzXn2PDWs3UqrMrcycNxkbNk6fOsu4YVNJTEgE4KaihVm26kPaN38wy/fXEzn7TX2ashHlSU1K4a1Rb3DueOZ26/T4f7ijc3MAtm/YxrLXPiU0PJTBc4YREhaKf6A/C6e8z6Ht2mOZcghJ05f6UCSiDI6kVH4Z8R4xORyv7lw0nJNrt6EX/+C9LH/T7n0HefWtBXz4xow8fV2Hw8G0tz9GR50iMMCfSQN7UaZk8YzlC5avZtXGzRQICaFvl7tp2bAWV2Li6PzMeCqVvQ2ANk3q8mjntnma25PyW42/Tzb8tdbJwLnrrmjYvJklL/nXvwNbYCBxkwfhV7EawY88Tfyc583C4BCCuz9J3LRhOGOjCez4ELbwQvjXaQJ+fsRNGYKtyM0ENGrhlWyl7qmPX1AA6/4ziaL1KlF3Yg829n01yzq1RnUjsHCBLI/Vm9yTkq1qcWnfca/kAghq3gxbYCAX+w8gICKC8AHPcHns+IzlAVWqcHH4SJxXrmQ8Fj5qJPErviZx/feEdOxIgYceJG7RYo/mqt6uAf5BAbzRZSJl6lai8/hH+bDfLABuKl2cevc2Y+5948EJ/ZdNZO+aLdTv0pzILZof3lxB5Ttq0H7EQywb/a5HcwHc2aElQUGBdO/wOLXr12DkpCEM7D0iY/kdrZswbPwAiha7KcvzxkwdRrPWTTi495BH87TvdCdBQUF0vOth6jeozQtTR9H7kQEAFCt+M0881ZN2rR4gKDiIb1Z/zE8bfqFW7Qi++vw7xo6cmuV3delkGomd77ubP8796fFGP0CE67190/Xedhz/KItc721okXCa9mzHnA6j8Q8K4Ll1r/DS7QM9nsFdnXYNCQgKZHqXcVSoW5kHx/diXj/TgLi5dHEa39ucF+8bC04nI5dNZvuazZStUQE/fz9e7jqBwrfcRP2OTbySLezOptiCAjnx8DCCa1el2Kh+nBkwOWN5UEQlTj0+nrTLWU9w/UvcTJG+XbD5+3kp1+3YggI52X2oyTXySc4MfCFjeXBEJU49MQ5HTrn6PODxXHd1aE1QUCDd2vehTv2ajJk8lKd7DgPg5uJF6d3vYe6781ECg4JY+t37/PLj74ye9Cwff/g53yxfzYOP3sfjz/Rg3qvv07x1U0ZMGHTV99cTGt3dhICgQMbdP5LKdRW9xz/Gy/2mAVC89C00v68lY+4dgdPpZMrn09m85ncat2/Knl92892Cr7m1wm08+/pwRnYc6vFs6cq4jlcr//MCxepVpOHzj/DDY7OzrFNvVDeCsh2vrLJgyTK+Wf0DIcFBef7aP2zaSVJKCh/NGM0ufYxXFixj7jiz7z0UdYqVP29mycwxAPQcNZ1GtRQHjh6nfYtGjHmye57nFf+cRxr+SqlyQCQwARgKbAJeAF4F6gMngXeBV7TWDtdz2riWK2Ar8APQUmvdSinVCtgAhGutY5VSTwIjgdKu13lRa71IKfUh0BJoqZTqqrUup5QKB2YBXQGn6/cO0Vqfcb2uE5gKPIk5uagHVATmAi2A88BnwAStdZLrOQ2B14FawBbMFQmP869Sk9TdWwBIO3oAv/Iqc1nl6qSdjCT4kWewFy9J8o8rccZcwb9mAxwnIwl9bhrYbCQuet0b0SjWSHH2x10A/LX9CDfVKp9leemOjXA6nJzdsCvL4xe2HubU6m1U6tnGK7kAAmvWJGnTZgBS9u8nQGVuN2w2/ErdRqERz2EvUoSE71aSsHIV/uXKkjRjEwDJe/dQcODAv3V56X9RvqFC/2S2x4kdRyhds0LGsstn/+Ld3tNxOkxPg5+/HylJydxS+TZWvbIUgMitmvsn9/FwKqNe4zr894ffANi1bS816lTLstzpcPB414F8vm5hlsd3btnN96t+4qFe93s0T+Mm9dnw/UYAtm3dRe26NTKz1q/J5k3bSU5OITk5hchjx4mooahVpzo1a0fw5XeLuXDhL8aNnMaff5wHIDQ0hBFjBnFf+0c9mjNd+YaKQ27vbSm39zb+Ugxz2o/CkeYgvFQxEqI9/cm6WuWG1dj7k7mqcGzHYcrWrJix7NLZv3it9zScDgcAfv7+pCalUL1FbU4fPMGgBWOw2eCTiQu8ki2kfnXiNm4DIHHXQYJrVM5caLMRWPZWbpk8GP+bC3P587VEf7EWW2AAt0waxB/Pz6Xs8rneyVWvOvH/3ZprroCyt3LL5CH4Fy3MleVrMnIVnzSYP59/jTKfe3Zf26BJHX7+wRxadm7bQ806ERnLaterzrbNuzK+A8cjT6KqV6aSqsDYoVMA2LZpF+OnPgeYXtxeDzzDiu+XeDQjQNWG1dj5k7kydHiHpkKtShnL/jp7gam9JuHI+Kz5kZyUzLfvf01qUgoAdj87KR68ApGTWxopTm/YDcD57Ucpmu14VbZjQ5wOR8Y6Vit9a0nmvDieMZNn5vlr79h/hDvqVgegtqrA/iOZnXSRp87RoEYVggIDAChb8hYORZ1m/9ETHDh6gr5jZ3JToXBG93uYYjcVzvPsniI1/v9MJ6Ap8DKwBlgN1AQGAwMwjXeUUuWB74CVQB3gc2BsDr8PpVQ9TKN7GFAF00D/UClVGRgC/Aa8BTR0PWU+5mTibsxJgRNYo5RyP8npAbQBegMBrqzHgLpAT+Ae4DXX69/k+nfswpwkLMKc3HicLSQUZ7xbI8GRBnbzFtnCC+FfrQ6Jn80nbuZogu7ugr1EKWzhhbCXuI34WeNI+vZTQvqN9EY0AsJDSIlOyPjZ6XBg8zPZCqlSlL3/dvbM/Pyq5534+nfw8mU0W4ECOOJiMx9wOMDP9MbZgoOJ/+JLLk+ZxqXhIwm97z78K1Qg5fARgpqZ0ofgO+7AFhzs8VzBYSEkxsRnxkpzYHdtM0dqGvGXYgDoNLYHp/dHcSHyHGf2H6f6nfUBqH5XfQK81AMUFlaAmOjMbZaW5sDPL7MH89efNnP50pWrnrdqxXqcXng/wwsWIPpKjFuetIw8YeFhREdnLouNjaNgwXCOHDrGzJde5/6OPVn97XpenJl5leeRnl355qs1XLx42eNZAYKyvbdOt/cWzHvdtFc7Bnw5mb2rNnslg7vgsBAScvmspaWmEev6rHUd25MT+yP5I/IsYUUKUrx8SV5/7CVWv72CPjP7eyWbvUAojhi3/VqaA1zZbKHBXProa86OnMnJfhMo8khHgqqUo/iE/lz8YDmpf/7llUwA9rBQ0txyOd1zhQRzecnXnBs5g1P9xlO4eycCq5Sn+IQBXFrwuVdyhYVn/U46sn0H3JfFxcYTHh7Ggb2aO+9pCUDbe1oQEhoCwC8/bcrx++sJIWGhxLttt+yftRjXZ63XuL5E7jvG2cgzxEfHkZyUTOFihRny2nMseXmRV7KlCwgLIdn9++l2vCqsSlHhvtvZMXO5VzP8L+5q3Qx/f2sKMGLjEwkrEJLxs91uIzUtDYDKZW9j+77DxMUncjk6lp0Hj5KQmET5UiXo370zH7w4gjaN6/LS/E8tyS7+fzz9SZujtT6klOoBbNFaT3E9flgpNQbTaJ8O9AMOaK3TG/taKdUUKJHD7ywLOIDjWuvjwJtKqcPAea31FaVUMhCvtT6vlKoAPAyU0lqfBlBK9QQuYBrz37p+53yt9T7X8r5ACjBAa+10ZXka2KiUGgk8BCQBA7XWKcBB1xWAezyyxdw4E+KxBWd+AbHbTSMWcMZEkxapcV65BECq3oNfmYo4Y6NJ2WFq+tMO7sZeopSnYwGQEpOAf1hm49hms5sDJVC+W3NCShShzbKxFChdDEdyKnEnz3P2x7zpTXHGxWEPDc18wGYH147LmZRE/LLPISkJJ5C0fTv+lSoS8+abFHx2CCFt25K0bRuOK54/SCbGJhDktkO12W04XNsMwD8ogAdnPEVSXAJfjDe9rT+8uYL7JvXmyY/Gon/axeWz3mn4xMbGUSAs8zK33W4jzbXNrBATHUdYuHsee0ae2JhYwtyyhoUV4MqVGLZt3U2Ca9zGym/XM3Lc4Ix1HniwE4/3GuK1vEnXeW8Bflu0ls2ffM9jH46mQtMIjv2232t5EmMTCM528M7+Weszoz+JcQksGf8eALGXY9j9vemJP7RpP7dUuNUr2Rxx8dgLZNuvubI5E5K4tHgFzkQzvib+910ERVQitEENAsveCgN64FconJKzRnP2uemezRWbPZctM1diEpcWfZWZa9MugqtVJKR+DQLK3AoDwK9QOCVmjeach3LFxmT9TtqyfQcKhGXu4wqEhRITHcOLz89m0vRRdO5yD79u3Myli5c8kuVaEmLjr/lZCwgKoP/MwSTEJvDe+LczHi+jyjL0jREsmraA/Zv2eTVjSmwCAWHu38/M41XFrs0ILVGEe5aOJaz0zaSlpBJ78gKn8+h45WvCQoOJT8gc3+ZwOvF3nXBWKF2Shzu2pv/k1yhdsjg1q5SnSMEwalYuT3BQIABtmtZh3ic39hDL/Fbj7+ke/6Ou/1cHWimlYtP/AO8BRZVSRcksmXH3Wy6/czXwC7BTKbVXKTUdOKG1zqkrL/3aqHZ73b+AApirANlzpmetAMS4PWcdZttUBmoAe1yN/nRe6cJLPbQX/zqNAfCrWI20k5EZy9KiDuFXqhy2sIJgt+NXqRppZ46TpvcQ4HqOvUwFHH/96Y1onN9yiFvb1AGgaL1KXD6YOehs59RPWNdpIj90nUbk0p/R81flWaMfIHnvXoKamPrkgIgIUo8dy1jmV7oUN8173TQ2/PwIrFWT1EOHCWzQgNgPFnJpxEhwOknautXjuaK2HqJqa7PNytStxDl9Msvyvu8+x9kDx1k+9v2Mkp8Kjaqx9YuNzH/0RS6ePE/UVs/W0qfbvnkXLe40A4tr16/BoQNHr/MM79q8aTtt7zI9l/Ub1ObA/sx/9/Zte2jStAFBQYGEFwyjsqrIwf2HmP36FDrd2w6A5i2bsmunaUyEFwwjMDCQM6f/7jCh/13U1kOoXN7bmyuUpOfb5qJgWkoaqckpGe+vtxzZepCaresBUKFuZU7pE1mWD3x3JKcORPHR2PkZJT9HtmQ+p1S1slw8fcEr2RK276dAS3NBNrh2VZIOZe7XAsvdRpklr5jvp78fIfWrk7j3EJHt+3Gy1yhO9hpF2pUYjzf6Ta59FGjRKCNX8qGobLlmZeaqV53EfYeJ6vAEp3qP5FTvkaRdifFYox9g26adtLrTXIWsU78mh/YfyVi2a/s+GjapS2BQIGHhYVSsUh594CjNWjZm7sz59H1oIA6Hg//+uMljeXJzcOsB6rVuAEDluooTOuv4rVHvjuP4/kjmj30zo+SnVOXSPPfmKOYMfoUdP3phAHk2f245RKk2tQEoVq8ilw5kfj+3TfuU7zpPYnW3aRxZupH981fl20Y/QJ1qFdm4bQ8Au/QxKrsG7AJcvBLD5ehYFk4fxagnHuLchYtUKnMbk95YxPrfzPu4addBIiqWtSS7+P/xdI9/ei2IP7AcGJ/DOlcwPex/66RDa52glLoLuB3ojCknGqKU6qS1/j7b6v6u310XrirauphDzvTn/Ab0zeHl06eGyT6A2CsFiqnb/ot/jfoUeH4uYCPh3RkE3tMVxx+nSd3xG4lL36fAyJdNgE0/4TgVRfK504T0GUKBia+DzUbCB3O8EY1Tq7ZSokVN7vx6IjZs/D7sHdST7YmN+oPTa72/I7+WpJ83EtSgATe9+QZg48r0lwl9sBtpp0+T9MuvJK5bT9G33sSZlkrC6rWkRkVhCwmh0OhROFOSSY2MInq257fb3jVbqNK8JgOXvwA2+GzEO7R4vAMXjv+B3c9GhcbV8A8MoGor04BcOeNT/jx2hu6vmpKLK+cusnTUfI/nAlj/3Y/c3rIxH3/3HjabjbGDJ9P76Uc4EXmSDWs2euU1r2XlN+to2fp2vl37CTabjSH9x/DUgD5EHTvOmlUbeO+dxaxYvQS73c5LU+aQlJTM1EmzmPPGi/R5/BHi4+MZNsjMMlSxUjlOnjh9nVf8Z/at2ULl5jXp73pvl414h+au9/bA+m2cPXCCAV9Oxul0on/cReSmA17Ns2PNZiKa12LU8qnYbDY+HDGPux7vxJ/Hz2H3s1OlcQT+gQHUaFUXgC9mfMzGT9fTY2o/xnxpxgd9NM47n7XYdb9S4Pa6lPlkFthsnBvzKkX63E/y8TPEbdhEzDcbKPPZbEhN5cpX35N85MT1f6kncq3/ldDb61H641dNrrGzKNy7CyknzhC34Xeiv/2BMp/OwZmaSvSK9SQf8d4EBQBrv9tAs1ZNWLbyA7DZGDVoEo8904PjkSf5fvXPLHz3Uz779n3sdjuzps0jOSmZY0eO8/LciSQnJXNYH2PiSM+fIGW3efXv1G5Wh2lfvAw2G/OGv0anJ+7lXNRZ7H52IhrXICAwgLqtTMnikhmLuP+ZrgQEBfDYxH4AxMfEZwwI9objq7Zya4sadFjxPNhs/DJ0PhFPticm8g9OrrP2eOVr2japy+87D9Bz5HScwJTBvVm0Yh2lSxSjVaPanPrjPN2fm0aAvz/D+nbFz8/OkN5dmDj3Qz5b9SMhQYFMGuiFWbfyUH6r8bd5ol7XbXBvTa31XqXUS0B7rXUdt3U6A92APpiBvx211vXcln+EKdHJMrgXaAA001pPdVv3F2CX1rq/UmoDsE1rPVwppYCDQF2t9U7XugWAj4CXtda/uwb3dtZaf+ta/pQrTzmtdaLrsUaY8QiPY0qHJgNl3Ab7vgp00VqX+zvb50rPtj77qVr5fU7VVdZrXcm7Dbd/6pUTJa2OcJXv4q3ttb+Wi0men5LUU/oUqWt1hBxdJNXqCLkaHhx7/ZUsYrP57O4WgPbnvXNV9p+oW8A7JaKe0Cm1oNURctVj1+Trr2QBx4WT11/JIkFVW/rcTIwVb67n1Z3G0Qvbferf7K0beM0DKiul5irjTuAdIM41q8/bQDWl1DSlVBVXTf1DXN1LDxAPTFRK9VNKlVVK3Q1UI7NUKBaopJS6TWutga+BRUqp5kqpqpjBuE0wJwQ5+QhIAxYqpWq4xhp8ABTQWl8BPnUtf1cpVVUp9QjwxD/cPkIIIYQQQuQprzT8tdanMLPqNMDMhrMYM0Xms67lp4F7gfuAPUB31zpXldBorTdjet6HARozLegsrfUHrlXeAu4Adiml7JiZerYCX2FODgoBd+UyJgCtdRzQDiiCqd3/xvW8R1zLrwB3YgYZ7wCGA7Nz+l1CCCGEEOLG4fTyf77GIzX+WusostXBa63/i6nLv4pSqgZwWWtd3e2xebhq6rXWP7r/Pq31IkzPfU6vvRIo5vbQZeCxa2S96pKLa4afdtd4zn7M1KDuJua2vhBCCCGEEL7Gqjv3lgeWKKW6A3sxc/D3wtx0SwghhBBCCK9zOh3XX+lfxFs1/tektf4GeBEzFkADUzDz5K+xIo8QQgghhBD/dlb1+KO1no65mZcQQgghhBB5zuGDdfjeZEmPvxBCCCGEECJvWdbjL4QQQgghhJU8cT+rG4n0+AshhBBCCJEPSI+/EEIIIYTIl6TGXwghhBBCCPGvIz3+QgghhBAiX8pvNf7S8M8DqZdTrY6QqzSuupGxT0iK9e2PZjK+d8OP6JQ4qyPkyt/uZ3WEXC28vNPqCDnqVbiO1RFydXPleKsj5Mrm49ex/S74XsBgH24KdOnwp9URcuW4cNLqCDmy31za6gjCh/nut10IIYQQQggvcuSzHn/f63oQQgghhBBCeJz0+AshhBBCiHzJKbP6CCGEEEIIIf5tpMdfCCGEEELkS/ltVh/p8RdCCCGEECIfkB5/IYQQQgiRL+W3O/dKw18IIYQQQuRLUuojhBBCCCGE+NeRHn8hhBBCCJEvyQ28xDUppcoppe61OocQQgghhBD/C+nx/999AGwDVnj8N9tsFHhmKH7lK0FKMrGvz8Rx9nTG4oD6jQl5uDcAaccOE/fWbAgKJnz4BGzhBXEmJhD76jSc0Vc8Hg2bjUYv9aFIRBkcyan8Nvw9YqP+uGqd1ouHc2rNNg4v/iHj4dL3NKBM50b8MuBNz+dyve5NYwYTWKUizuQU/poyi9STZzIWFxkxgKA61XHGJQDw57DnKfx0LwKrVALAr2gRHLFxnOs9yMOxbHSb+ji3VStLanIKn4x6hwvHM7dZq8c7UK/z7QDs37CT1a99TnB4CH1eH0JgSDBpKaksGvo6Mec9837abDZemjWBiOqKpORkRgyeSFTkiYzlj/TqyqN9upGWmsZrs95h/ZqfCAkNYfqsCZQuW4qAgAAmjHqRndv3cH+3jjw1oA9paQ4+W/IFixZ85hPZTp86y5vvz8x4XvWaVXnphdks/mDpP8o2fdbzVK9RleTkZIYNmpAlW49e3ejV90FSU9OY88rbrFvzI6GhIbz86kRKl7mNwMAAxo2cxo7tewAICQnmsy/fZ9ig8Rw5HPn/znWtvPdPfYyS1cqQmpzK56Pm85fb565pz7to0LUlTqeT7+d+wYEfdng8Q7ZAvrlfs9kIfXoo/uUq4UxJJu6NmTjOueWql5kr9ehh4t8xucKem4AtrCDOpATiZntpf5slpo1JM0ZTtXplkpNTGDd0CiciT2VZp0jRwny2cgGdWjxMclKy1/P0mfokZSLKkZqUwnuj3uSP4+cylt/zeCeadG4GwK4N2/nytaUEhQTRf+5QwgqHkRSfxFtDXyPmYrSngxHcYxD2UhUgNYWEhbNxns88DvjXaEhg50cBcBw/QuLHrxN4z0P412hgnh4ahq1gEWKHP+zZXIDD4WDa2x+jo04RGODPpIG9KFOyeMbyBctXs2rjZgqEhNC3y920bFiLKzFxdH5mPJXK3gZAmyZ1ebRzW49nu57d+w7y6lsL+PCNGXn+2nklv9X4S8P/f2fz1i8ObNIMAgOJHtEffxVBgcf6EzNtnFkYEkJo36eJHvsszugrBHfpjq1gIYJa3UXq0UMkfLqQoLb3EPJQL+Lffd3j2UrfUx+/oADW/OcFbq5XkfoTH+GnvrOzrFNnVDeCChfI8liDyT0p2aoml/Yd93imdCGt78AWGMi5PoMJrFmNIkOf5vyw5zOWB1atzJ8DRuO4nHmgufTKW+Yv/n6UeH8Of0151eO5arZrSEBQALO7TKBc3crcP74n7/Z7BYCipYvT4N5mzLpvHDhhyLIX2L1mM5WbVufMwZN8PX0JTR9uQ9sn/8NX0xZ7JM89HdsSFBTEf+7uQb0GtXh+6gge62FOdooVv5nHnupBh9YPEhQcxJerFvPzhl95ZlBfDh44wpBnxlKtehUiaih2bt/DhMkjaNP0XuLi4tnw+9esWL6KK1f+/wdyT2br1rkvAPUb1mbU+CEsWfj5P9pu7TvdSXBwEJ3adadeg9pMmjaSPo8MzMj2xFOPcnfrrgQFB/H1qlzv0/8AACAASURBVCX8tOEX+g9+jIP7DzPo6dFUq16F6jWqsmP7HmrXqc6M2ZMoeest/yjTtVRv1wD/oADmdZlImbqV6DT+URb2mwVAaJFwmvZsx5wOowkICuC5da9w4PaBXssCvrtfC2jcDFtAINGj+uNXJYLQx/oT+2JmrpA+TxMz7lmcMVcIvt/kCmxpciV+tpDANvcQ8mAv4t/z/P7W3V0dWhEUHMhDHR6jdv0ajH5hKP17PZexvFnrJgyfMIibi93k1Rzp6t/diICgAF64fwwV61bhkfF9mN1vOgDFSt/C7fe1YOK9o8HpZMLn09i6ZhMRt9ckcs9Rvpq7jOZdW3PfoK4sfmGBR3P517kdAgKJn/4sfhWqEvzgkyTMm2QWBoUQ1LUf8a8MxxkbTeDd3bCFFSJ59WckrzadFiGDJpO8/D2PZkr3w6adJKWk8NGM0ezSx3hlwTLmjhsAwKGoU6z8eTNLZo4BoOeo6TSqpThw9DjtWzRizJPdvZLp71iwZBnfrP6BkOAgyzIIz8u3DX+lVFngNaANkAB8CgwHDgOzgfuBRsARYJzW+hul1IdAS6ClUqqr1rqcJzP5R9QiZdtmAFL1fvwrq4xlAVVrkBYVSehj/fErcStJa7/FGX2FxK8/B7up2LIXuwXn5UuejJSheCPFmR93A3Bh+1GK1iqfZXmZjg1xOhyc2bA7y+Pntx7i5OqtVO7Zxiu5AILr1CDh1y0AJO85QGBElcyFNhsBZW6j6Pih2G8qQuyK1cStWJ2xOPyh+0j4fRspRzzf81qxoeLAT7sAiNpxmNI1K2Ysu3T2L97q/RJOh+lp8PP3IyUphTMHT3BLxVvNvysslLTUVI/ladSkHhu+/y8A27fuplad6hnL6tSvydZNO0hOTiE5OYWoYyeoVl3Rqs0dfP3lapZ8Pp/YmFjGjpgKwIF9hwgvGEZqWio22z/vMfFktnRTXh7LoCdH4XA4/nG2H9anZ9tF7To1MpbVrV+TzZu2Z2SLjDxBRHVFqzbNWPHlKj5Z/i6xMbGMGT4FgMCgQPo+Oog33nn5H2W6lnINFdr1uTux4wilalbIWBZ/KYY57UfhSHMQXqoYCdFxXsuRzlf3awERtUjZYXKlHdqPf6VsuY6bXPZbbiVpncmV9E3WXA4v7W/d1W9ch43f/wbArm17qVmnWpblDoeTPg/054v1nukguB7VsBq7fzJXiY7uOET5Wpn7tYtnLzCj1xScru+c2a8ls2bBt9hc263orTdz5YLnr5L4Va5B6t6tAKQdO4hf2czjgF+lCBynIwnq9hT2YiVI2bgaZ2xmBv+6d+CMiyVt3zaP5wLYsf8Id9Q1+7TaqgL7j2R2hEWeOkeDGlUICgwAoGzJWzgUdZr9R09w4OgJ+o6dyU2Fwhnd72GK3VTYK/lyU/rWksx5cTxjJs+8/so3sPw2nWe+rPFXSgUB64AQoAXQBbgXmOhaZTLwNtAAcyLwgVIqEBgC/Aa8BTT0dC5baCjO+MwDsdPhALufWVawEAG16hD/4TtETxpJ8H+6Yb+1lFnR4aDg1NkEd+pC8tbfPR0LgIDwEFKi47Nks/mZj08hVYpy99/OrpnLr3re8a834e3vlK1AKI5YtwZMmgNc2WwhwcR8+hUXxk/nz4FjCO/WmYDKrpMWf3/CH+hE9OJlXskVHBZKQkzmNnOkObC7cjlS04i7FAPAvWMf5dT+KM5HniXucixVm9di7LpZtH2qM79/tsFjecLCCxATHZOZx+HAz898vsLDCxAdHZuxLC42joIFw7ipaBEKFS5Ij65Psm71jzw/ZTgA+sBhVv24jA2/rWD9mp+Idvu9VmcDuKt9aw4dPMrRI1H/KJd5/bAs2dLS0tyyhRGTLVt4wXBuKlqEwoUL0v2Bfqxd/SMTp44EYMumHZw5fQ5vCg4LITGXz136z7f3aseALyezZ9Vmr2YBH96vhYbijLtGrpp1iF/4DjGTr84VPmU2wR27kLLNO/tbd2HhBYiJyfyMpaVlfjcAfv1pE5cvebfcyF1IWCjxuXy+0lLTiHXt17qP683xfZGcizwLmO075pMXaNenAzs3eL6BbQsOhQS344DDkXGSZgsrhJ+qTdLy94h/bRyBd96P/ZbbMlYN6vAwSd985PFM6WLjEwkrEJLxs91uIzUtDYDKZW9j+77DxMUncjk6lp0Hj5KQmET5UiXo370zH7w4gjaN6/LS/E+9li83d7Vuhr9/vu0f/tfKlw1/4E6gLPCo1nqn1voX4CkgvcDzE631p1rr/cALQFGgnNb6CpAMxGutz3s6lDM+HltIaOYDNhs4zM7BGRNN6mGN8/JFSEwgZd8u/CtUzlg1evxQroweRPiYyZ6OBUBKTAL+YZk7Lmx2nGmmV6dCt2aElijCXcvGUuHB5lR7sj0lW9XySo6cOOPisRdw2252m2n8A87EJKI/+QJnYhLO+AQSt+wksIrpoQpuXI/E7Xtwxnqn1zMxNp7gAsGZsew2HGmZvc/+QQH0em0QwWEhLB1vLjG3H9KV9e98w4t3PcebPafx2FvDPJYnNiaOsLDMUiy7zUaa6+ATk21ZgbACXLkSw6WLl1m7ypx8rFv9I7Xq1KBa9Sq0bdeCpnXa0bhWO24uVpRO97bziWzpHujWiY8WeuaELiYmNms2u90tW+xV2aKvRHPp4mXWrDTjXNau2kBttysY3pYYm0CQWyPDlu1zB/DrorVMbfQM5RtVpWLTCK/m8dn9WrZcNvdc0Vlzpe7bhX/5zFwxE4YSPWYQYaO8s791FxsTR4GwzJx2e+Z3wwoJsfGEZGnE2rN8vgKCAug/91lCCoTwwfj5WZ77UveJTOk2niFvj/R4LmdiPAS7HaPsNtP4B5yx0aRFHcIZfQmSEkk9tAd7aXMcsJcsgzM+Lst4AE8LCw0mPiEp42eH04m/6+StQumSPNyxNf0nv8YrHyyjZpXyFCkYRqOaVWlYsyoAbZrW4aDbuCLhWU6n06t/fE1+bfhHAMfdG+9a6zVaa1fhN4fc1k0vXA7wdqjUA3sIaNAYAH8VQdrxzPKT1CMav7LlsRUsBHY/s/xEFMFdexDY2tXoSkzI2NF52p9bDnFbm9oA3FyvIpcPnsxYtmPqp6zuNIl1XadxbOlGDsxfxdkfd+f2qzwuaec+Qu5oBEBgzWpZynb8y5aixII5pufH34+gOjVIPnAYgJDG9Uj41Xs9nse2aiJa1wWgXN3KnNFZd9z93h3BmQPH+WzsuxklP/FXYjN6a2P+ukJweAiesmXTDtrc1QKAeg1qccC1HQB2bttDo6b1CAoKJLxgGJWrVEAfOMzm37fT1vWcxrfX59DBI0RHx5CYmERiQhIOh4ML5/+iUOFCPpEtXc06EWzd5JlBq1s2badtu/RstTm4P3P3sGPbHho3rZ8l28EDh9n8+zbatmsJQJM7GqDdsnlb1NZDVG1dB4AydStxTmd+V4tVKEnPt4cCkJaSRlpySsZnz1t8db+WcmAPAfVNLr8qEaS65zrqyhXulutkFMEP9CCwlStXkvf2t+62bd5FyzvvAKB2/RocOpB3n6WcHNp6kNqt6wFQsW4VTuqs47eGvjuaE/uPs2Ds2xklP537d+GO+833ISk+8aoTUU9IO7IP/5rmOOBXoSqOU1EZyxzHD+N3WzlsYQXBbse/QjUcZ8z+2L9aPVL3bvF4Hnd1qlVk4zYzuH+XPkblsplXGy5eieFydCwLp49i1BMPce7CRSqVuY1Jbyxi/W/bAdi06yARFct6NaPIP/LrNZxkrl2AktO0CF4b1Jvxor9tJKBOAwrOmAc2G7GvTSf43gdJO3uKlM2/Er9wPgVfMLV2Sf/dQNqJSBzRlwl7dgzBd3UAux+xr033SraTq7ZSskUN7v76ecDGb8PmU+3J9sRE/cGptdu98pp/V/yG/xLcpB63fPAaNpuNC5NmEt7jAVJPniHh59+IW/U9JRa+jjM1lbhv15FyzByo/MuWJvXbdV7LtXvNFlTzWgxdPhlsNpaMeIvWj3fk/PFz2P3sVGpcDf9Af6q1Mo20b2Z8wnezltL95ado9mg7/AL8+HT0/Ou8yt+36tv1tGjdlBVrPsKGjaEDx/Nk/95ERp5g3aoNLHhnCV+sXIzdbuPlqXNJSkrm9VffZebcF/h6zRJSUlMZ8vQYTp88y0cfLuXLVYtJSUnheORJln78lU9kA7ipaBHiYuOv84p/38pv1tOi1e18s+ZjbDYbzw4Yy1MDehN57ARrV23gvXc+YsWqj7DZ7UyfOoekpGRemzWfWa9P4du1n5CSmsrgp0d5LM/17FuzhSrNa9J/+QvYbLB0xDs0f7wDfx3/g/3rt3H2wAkGfDkZnE70j7s4tumAV/P46n4t5XeTK/zlediwETt3OsH/eZC0cyZXwuL5hE8yuZJ/ceW6cpkCz44h6E6TK26ud/a37tZ9t4E7Wjbm0+/ex2azMWbwC/R9ugfHI0/yw5qfvf762W1dvYkazWrz/BcvYrPZmD/8Ddo/0Zk/osx+rWrj6gQEBlC7len0+GzGR/y09HuenjWYVg+1xe5nZ/6INzyeK3XHL/hH1CN01Gyw2Uj8cBaBdz2A48/TpO76ncQvFhD67EsApGz9CceZKADsJUqRut+7x7C2Tery+84D9Bw5HScwZXBvFq1YR+kSxWjVqDan/jhP9+emEeDvz7C+XfHzszOkdxcmzv2Qz1b9SEhQIJMG9vJqxvwsv83jb/PFyxDeppRqD3wJ3Kq1vuh6rBcwECgOvKK1fsP1eDkgEqiptd6rlNoAbNNaD8/xl+fgr84tfXYjr9pW2uoIOWpewrt10P/UrItFrY5wleVX9lod4YaU5vR+r+3/R6/CdayOkKtRNb1XFvFP2Xz8OnaTX70/oPp/1Si0jNURcvVmu9jrr2SRoOfGWR0hR/abffO4DhBwcwWvd6L+r8JCy3u1jRYbH+lT/2Yf30V6zVrMbD0fKqVqKKWaAZOAlX/jubFAJaXUbdddUwghhBBC+Cynl//zNfmy4a+1TsPM4uMPbAaWAUuBqdd6nstbwB3ALqVUvtx+QgghhBDixpNfa/zRWh8FOuSwqFy29aJwq+/XWq8EinkzmxBCCCGE8L78VuMvPdZCCCGEEELkA/m2x18IIYQQQuRv+W2SG+nxF0IIIYQQIh+QHn8hhBBCCJEv+eLMO94kPf5CCCGEEELkA9LjL4QQQggh8iWp8RdCCCGEEEL860iPvxBCCCGEyJfyW4+/NPyFEEIIIUS+lL+a/VLqI4QQQgghRL5gy2+XOIQQQgghhMiPpMdfCCGEEEKIfEAa/kIIIYQQQuQD0vAXQgghhBAiH5CGvxBCCCGEEPmANPyFEEIIIYTIB6ThL4QQQgghRD4gDX8hhBBCCCHyAWn4CyGEEEIIkQ9Iw18IIYQQQoh8QBr+NwClVBmllC2Hx/2UUvWtyOTLlFKFrM4ghBBCCOFr/K0OIP6WSKAEcD7b42WAjUBonidyUUoVB2oBAUCWkxOt9UpLQsFupdT9WuvtFr3+dSmlwoCqQAhXb7efLco0EViitT5ixevnxldzge9mU0r1Aj7TWidle7wA0E9rPceaZBk5CgMPATWANGAXsFRrHWdlLvD5bBWAakBQ9mVa6y/yPpH4N1JKHQMaaq3/yvb4rcBOrXVxa5IJT7A5nU6rM4gcKKUeA552/dgA2AmkZlutBBCrtY7Iy2zplFKPA29iGv3ZObXWfnkcCQCl1HGgi9Z6mxWvfz1Kqa7AIszBO/uVHCu32w9AC2AbsAT4VGv9pxVZ3PlqLvCtbEqpQExnjg2IBipwdWdBA2CN1jokj+NlUErVBdZi9me7MVeeawHJQFut9SHJlmO2EcB0wAmkZFvs1FrnWQeQUirSleO6tNYVvBwnC6VU/7+7rtb6TW9myc7Ht9t/gGauH4djju3x2VarBDSThv+NTXr8fddnQCnMQbwB8D0Q67bc6fr587yPlmEE8C4wRmsdY2GO7D4F1iqlPgOOAgnuC/N6Z5+D6cBCYCpw2eIsGbTWbZRSJYHuQE/gFVfD9iPgS6t6PH01lw9m6wXMJ7NhcSyX9b7Lmzi5mgd8CzyltU4GUEoFY7LPA+6SbDkaAYwDZmitHRbmAHjF7e+3AUOBD4HNmJOk+sDjwKt5nsxsp7/DiWnc5iVf3m67gGfJ7Iyq68qULr3N0TuPcwkPkx7/G4BSqjemJzHpuivnIaVUHFBHa33Y6izulFJR5N6r4szrnpTslFIJQITWOtLKHNejlKoE9MEcnAC+AuZprX+1LBS+mwusz6aUaoHppf4BeAC46LY4/cC9R2udvcc4zyil4oG6Wmud7XEFbNNah1mTzOeznQfusPKqQ06UUhuARVrrD7I9/ggwTGvdwJpkvs2Xt5tS6gNgiNY62qoMwnukx/8GoLVeqJSq6xrIm1MtvVU92GuBtoBPNfwxDZwlmNrrA1aHycEvQCPM2A2fo5QqBnTD1Dnfjikz+xgoCaxUSs3TWo+TXL6XLX18iFKqPHBCa+2LPTs7gVaAzvZ4PcDq76svZ5sHjFZKPZ1+NcJHNAKeyuHxbZjxCJZyjUOrDKSXUNowZZb1tdYvWRbMh7eb1rqvUsrf1ZGRU5tjvzXJhCdIw/8GoJQaB0zB9N5lL6mx4nJlul3Aq67awENkvSyI1nqkJangeeARYIRSah+m7OITrfVZi/JkrzvdDbynlGqCKcdIc1/XqhM5pVRfTMlKK+AUpuH6pHvvp1JKA3MwJQf5OpevZVNKLQWecPXSzXQ9luO6WusHvZnlOpZhSqLqA//F1NPXwzSC3nf/rljwXfD1bD8DjyilzgFZyn0svJK5G3hWKTVIa50GoJQKAMYAWyzKhCvH48BbmLaOk8wGrBOT28qGvy9vt47AB0BRchiHRuZJlLgBScP/xvAEMEFrPc3qINm0BDZhZqapnW2ZZT2NWuvFwGJXL+xDwMPAS0qp/2JOApZbcAkze93pBeC+HNaz8kTuFcyYkSla6425rLMbMzYhL/lqLvCtbHFkfu8sn4HmGgZjPv93kbVm/gJwr9vPVnwXfDnbR0AUZgxT9kGXVhoMrAL+o5Tag2ko1sZ0aNxpZTBMI/p14EXMFZvbgZswdfXvWRcL8O3t9iLmJPMFzEQB4l9EavxvAEqpaMxlSV8rqbkhuKbA6ws8BwQCSZhSoHFa6+yznlhKKWWzqjxDKRXoYyUEgO/mAt/OJv5dXGODavtajT+AUqoIpoOluuuh3ZhxaZY2GpVSSUA1rfUxpdQa4E2t9Qql1F3AHK119ev8Cm/n89XtFodpcxy0MofwDunxvzF8DvQAJlmc4ypKqVuAgZgdlx3Tq/Ku1jq3GUXyKtdtZPb218fMmjAC01tWAlMv+zXQ1IJsx4AGWuuL2R6/FVNjbNVUaf5KqQFABFfXw9bTWleVXFfxyWyuG/71AdZrrU8qpUYDj2K+B0OsnoVLKRUKlOfq+eidWusdFkTK4MPZtgAKU1bpU7TWl5RSizG19AeAAKs/Yy5XMFekwWy32sAKzBiOchZlyuDD220zUBOQhv+/kDT8bwwJwBilVDfMQNrstfSW1OsqpRoB64CTwK+YBk8nYJBSqpXWeqtFuX7GXNKNwlwefyTbDZb+Ukq9Abyfh5nc50guB0x2zSDirlJe5cnF25hyhp+ADsA3mANSNaythfXVXOC72aZjSgTbuQboTQFmYMpXZruWWUIp9Shmu1118zosrh/25WyYq5QLlFKfYKYpzjIzk4VjgwIxY1iedD1UBXhZKRUC9NBaX7Eil8s6zDi0pzDHqAlKqYWYMWB/WJjL17fbl8B8pVRrch6/Z/WU2OIfkIb/jSEUM2jQ18wCPgGecS9PcTWqZwKtLcq1Bxiltf7tGuv8BNTJozxwY8yR3BFzwPlWKbUfeF5rvUcp9R5QWnLdUNkeBbpprbcppeYDP2utxymllmNm47Ks4Y85IXofM1d5ooU5cuLL2UZj9hGdc1hm5digKZiOlhaYzxaY/f8CzHZ83KJcYKbVXYTpkHob6IeZTS2VzAa3VXx9u10G2rv+QObgaCs/a8IDpOF/A9Ba97U6Qy4aYGYRyV6T/jpmSjJLaK0H/I11znP1XU29Rmt9HGgDPj1HchjmBAVgH+b93YPpIV6b25PygK/mAt/NVoTMaXY7YPKAKX0ItCRRpoLAG67vhK/x2Wxa6/JWZ8jFg0BPrfWvSikngNZ6s1KqH6asxjLa3EX7HreH2iilIoDLWuszFsVK57PbDXPVciJwWmv9NoBSahPm5n++NsmI+B9Jw/8Gocyt5HOqI66vtc5pLuC8cBZTtpJ9zusKXD3tqHDx4TmSo4AamNKtg5ixER9gpg0sZFEm8N1c4LvZ9gC9XNM+3gqscE0V+BxmHImVFmPGH1hyz4Xr8NlsrrEHudJaWzXTT3HgXA6PR2OuVlvG1cjPSWGlVGGL56P32e2GufJwL1nvMzAPczIQhA9+P8TfJw3/G4DbPP6xQAFMr116o2KlVbkwB8n5Sqlngd9djzXF9C4utiyVj/PhOZLfBj5WSvXB3HF2o1LqAqZky5LxGj6eC3w323OuPDcBL2qtjyil3gS6YsqTrDQT2K6U6oE5cco+H30bK0K5+HK2WK49TbJV+42fgSHAINfPTlf9+gTMvRCstJes8/fj+tmJeW+tvPrly9utO3C/e7ms1nqRa2KKz5CG/w1NGv43hqeAEVrrWUqpU0ATTI3icqy90cc0TG/iUsyMPjbMgLPXkR3DtfjkHMla69lKqbPAJVdteH+gP/AX5gAluW6cbIFAGSBIa33J9dhLwDCttdW164sxjdjv8K356MG3s2UfM+UPVMSc5I3K+zgZBgNrXFNkBmPmyK+MmY++nYW5wMzO5C59m03B9F5baQiw2ke3Wwjme5DdJay/yir+IZnH/waglEoGqmito5RS3wILtdbLlFJ3AB9qrStbnK8QZpq5BOCI1jrByjy+TuZIFt7muurQRmu92+os2bnmo28s2TxDKdUGmKm1rm9hhiDMTDkRmMb1AeAjC8uPrkkp1Rhz7KxmYQYb5gS9O2Y6bJ/ZbkqprzAlPT3Sp51WShXGnJz4a607WRhP/EPS439jOI8pC4kicy7iZcBpTI+7ZVzTfe11DUrqgblD7mZMeUGqldl8mM/MkayUWvB319VaP+bNLO58NRf4djY3RzD7CV9swGqgsNUhcuHL2XJzBtPgtozWOkkptYys89H7ZKPfJQkoa3GG7UBvrfWHFufIyWBgPXBaKXUcUxpVFrNfufdaTxS+Txr+N4YVmFr6x4ENwDyl1DrgAcCy2SeUUsOBqUBbpVQZTN36MsyUlOGYG2aJq6XPkdyKnO/LkJdTpYW7/T0QM1XgQUwJWTJQD9OAzOvpZH01F/h2tnSHgQ+VUmMwc75nuQpn1b0/XF7CZHuDnOejt3Lcks9mU0p1yOHhQphSn105LMsTvjwfvav0LrtCmPJZq+voS5Jt3+8rtNYnlFI1MPf9qIbJeQhYq7V2XPPJwudJw//GMBwzr28NzE1cHgC+x9TgPWJhrmcwN8f6RSn1GrBNa93DVYK0DGn45yZ9juScDuR5Okey1rpb+t9d873P0VoPc19HKTUVyNM70PpqLl/P5iYVM3+5L/rE9f9Xclhm9U2yfDnbtzk8low54RyYx1nc+fJ89NmPQU7MNtuI9ePQ5gPfKqXeAY5x9cm5lSfAaK2TMWNdvrMyh/A8afjfGG4G+rudafdRSg3E3GCmtnWxuJXMwcUdgYWuv5/GzIctcuDD83F3x/RWZ7cQa6eA9NVc4KPZfPjeH2it7VZnyI1k+3/x2fnofXhfCzDe9f+Xc1hm9Umm+BeThv+NIRIogdsNp7TWsUqp8pieC6vm/D2CKfM5hZm7P30n/yg+UL/uy1xzcpfHDKBy59Ra77AgEpg5pduQeeOndJ2wsKQM380FPpzNNYDxOUzpRWfMSUqk1nqZlbnSueZYV5he4uJAVA43A7SEj2e7GTPrSvb7f5ywJpFPz0fvPg7tvGscWnfMOCtLx6H58Imc+JeThr+PUko9Bjzt+tGGmS4t+06qBGbAr1WeBz7FfI4+0lrvVkrNwpQA3W9hLp+mlHoUM//7VQdvrO3pmQy875olZDsmW2NMSVK3az0xn+YCH83mqgdfBnyEacAG4PqeKqXCtdZ/e4CyF7IVxOw37sF83itjSkIqKKU6aK1PSbYcs7XAlHpmn9DBhrX7DZ+dj97Xx6EppeyYz1oVzIw5VYCDPnhXd/EvImecvuszTE1nen3d92TW233nWjaHrLcjz1Na6y+B0pipKXu5Hl6ImXp0jVW5bgAvAe9jrpKUzPbHslmatNaLgbsxjYiemCs3CUALrfXXkuuGyjYZeNZ1V+9UAK31i5ha8JEW5gKYhbnSVYrMufIHY8a9zLEqlIsvZ5uHOblsAdRy+1PT9X+rDAY6KqUOkjkffRRwB2Y8k5UyxqEBvXCNQ3P9vYeVwZRSJTHlgJ9ixpTcBIwGDlzjjsNC/GPS4++jtNZxmIM3Sqko4FOtdZKVmXJxEQhw9aaAuatwqFKqu9b6k2s8Lz8rCLyhtba6TOUqWusNmJmjfIZSKjS3XEqpdlrrtTk8Lc/44jbDzMSxPofH/6+9u4/WdK73OP6eaXmYdBZ19OSUqMknlOeHQgw1qEaESDU5atDK0VGGkyiJySxkIinK8XQckumBMiMi6iCiqEwfoU7SGYQMw2hinz++1z373rd7b9ns63fd9/6+1tpr7X3de6/9XXtmX/t3/X7fhx8BJ9ccS6d3AdNs/1kSsKyLyIGU/zk2ObbJwC627ywcxxC2fydpbYb28T+PBvSjp9l1aCcBtwGbAn+prn2QGCL3JcoP8Up9Khf+veFcYG9JV9i+W9KniBvEDcC/236kRFCSdiI6N7yky8sPMdghIw11LvCvIlvBYQAAEnhJREFUlO8qgaQLgRm2F1XvD6tgC8jLJe3Y/v9c0suIP5x7ULAITtKZxG5/p1b3kHuAubYX1BoY/AnYhKgPajeV8nURL6Kjg0nlBZQ/hW5ybLcSi/9GLfxhSB9/EZNn3ZBBjk2uQ9uOOBl8ou0h8zFJhwM/KxpZ6mu58O8NxwIzgO0lTSbapx1H/BGfU71WwheI4rcTiZ3EdxN1B3OIosLU3fHAzVWh2R+AIX2RbW9XYyyLGVy4Lq7x+z4bfweulDTV9l8l7QfMBu4j0mxKWkSkz9wAXFdd25RIc/gukQp3mKTdbM+vMa5ZwGnV/eIFwDRJaxB1QweO9IU1mA98rqp1gcgJfymR7tDtlKJOI8V2ed3BdPTuv5SYMTCbaP/4ZPvnlmr/KGlFIg3pA0QtCcASSV8HDrb95LBfPPaaXIc2gUiN6rQqDe3vn/pDLvx7wweB99q+qeobfo3twyXNJRbepRb+rwd2t21JNwMr2b5Q0lKiVdkFheJqunOJGQw/YDCPuIj2to8NbgG5PVGQd42kRcAGxML2BNtLR/zKsfdaYLbtIac3kj4DbGR7WvWgcgyxqKyF7XMk3QscRjzQHUVMVH2/7e/WFccwDiSG2D1AdH25gkjJuI2415U0UmzTC8TTrXf/nC7XShb3ngRMAfYkHoAnEgXuJxATcv+jUFzY/o6kVwOr2W611z0bmFOyULsyF/iipPdTbb5IWo94iCraBjX1t1z494YXM9gu8J0M3vgfJiaHlvI4g7vVtxMzBeYBNxHdCVJ3mwKb2761dCDDTAPtZsD2vDENZhjVUfh7iILoDxLH49eWiKWL7YBPdrl+AbHohljwd1usjamqwL6JRfbnE/exDYB1ib9DC4BfED+rjUsFZvteYIuqBWQrX30BcHmJdp490vJxD2An2+0dfL4t6UHgQgou/CtNrUP7JPB1Ih0Q4uFyOeAS8sQ8jaFc+PeGXwEfkrSQ2H36nqTliJtDyQFG1wCfkfRx4OfAvtUx6hQiBSJ1Z2CV0kFUuu0odlPrjuIw9QYTgaXA+ZKW5cAWrD0AuJtIN+rs478jg73NX0PUvNRK0lTiYbxbz/fP1xzLFGIhDbANsB9x6jVA/JtOJmYNvK7OuDpJuhLYtbNgW9JLJc23XeyhpMEeJ/4NOz1cdyCdmlyHZvtRYK8qp39tqodM27eXiimND7nw7w0ziePnlxBDR+6QdCqwO9GpoJSDid2JDxN96Q8iFvzL04DC1QY7lsjVPYUo1BvyR7POXN0G7ygOV2/QtPSxzxK98acQ3UMmEjvWOwH7VG35zqPmBYako4nfwTuJVpTtBqg6htXoAeI+NqF6+zeG5qgPEA8Cte90dnko2V9SZ8OEtSn8UNJghwHfkLQ/cL3tpyS9ETgVmFUNKwSieLXm2BpbhybpLmAT23cRNRut66sBv7T9smLBpb42YWCgEcMI0zOoBn2sbPuh6uNXA/fbXlI2MpA0yfbj1Q1+CvCA7exKMAxJT43w8oDtkl1qViSK9NYlFrELiFayxXfvmkrSFsABRC/1pcCvgZOqmpzNgM2AU22P9O/+fMd0H3CY7TPq+p7/KElXEbvqtZ+CdCPpTURO9QTidOZPdH8oOamJP8/SJD1EdEOaSPzcniJSVlqDxZap+94maQmwflWHdiVwvO15VergEXWf4Eh6N7BV9eFM4uGo82FoMrBVLvzTWMkd/96xKrBeleKz7NheUrFuDtX3nwhsq+hHdibRj7h0m7RGa+ouu6S1iN2xlYh86wlEb+4jJW1t+46Csb2GWFyvRXTk2IGYcHl9qZhaqnqDrjUHtm8gCh7rtgKxw9k4trctHUM7278iirRbDyXvsT3klETSCsBGBcLrBbuUDmAETatDu4U4GW/9Dd+QoR18Wg+Ze9ccVxpHcse/B0j6CLEzsFyXl4vtEFeTBy8D1iA6YKxFdHLYHJhq+7YScaXRkTQPWAJ8oHUkX53inA2saHunQnFtTixi/4c4UVqbmBb6MWA325eUiKuKbRLRInNjBnc5lylVfyDpG8A9to8s8f17laS3EGmL6/D0vv0DtnOz7BlIWp44/brddtFaL0nfI9JPDyRSY/cldtz3Ao61/S8FYzuTmMOT9XCpVnkT6w2HENX/h5Ua1jWMnDzYX7YGNmvPw60GyhzFMDvaNTkeOMb27Fbute2DJP2FmGlRbOEPnA7sSnSjKb3IaS+IXomoMdiZyPPv7PlesiC6yeYA9wPvIx549yVmMRxOubbJjVbNijiD6N5zK3GvWA94WNI7Cp/KzQQupoF1aLb3kfS66tR+UdVJajfgBtvnlIwt9bdc+PeGVxP5pU1a9ENOHuw3D9G929AqdO/aUZcNgW4zBs4DPl1zLJ12JHrjN6Hv9uKO93Px8OytT9VqV9JBwL22z69qJg4i5kmkob4MPEIMI5wOvIqY4LsPUVS7RbHIYnL1XODx6m/T5sCPiYe74wrGhaTpRHrsDpLuJua63Ex0+lnN9uyS8aX+1chc4/Q0PwTeVjqILnLyYH/5DvBVSRu2LkjaiBgo8+1iUUVHmNd3ub4pcG/NsXRaSuQOF2d7n9YbcCTwkfZr1fUZwCllI220vzN4cnM7MWsAorXnukUiar63Ap+wvZDI9/+B7d8Rp9QbjPiVY+8Eok7pDgDbi4kHFRGnhSV9GjjQ9o+Ih6Q7bG9FxPvRopGlvpY7/r3hFuDEqiPA7XQsqm0fWiSqnDzYbw4n/k1vkvREdW154oGg25CqupwCnCbpU8TD5gaSphGtNE8oGBdEashsSTNs3184lna/J9oWdsa0OvAToiYnPd3PgI9V/9duIdqyngi8kdzMGM4SYkDWSkQ71A9X119B+V7+exIdpK5rXaimWt8FfJOy6T5rMjhHZRqDKYsLgOzok8ZMLvx7wzbEH6RJxFF0u5LV2cNNHryYnDzYc6ois6lVD+51iI4YC0p286niOqHK7T+WWLBeRAzHOoaoMylpDyKfeWEVY+dDeW1/wCV9mMGdwgnAZZL+3vFpryBSMlJ3hwGXAvdR5a1Xi8SXE/e69HSXET+bR4jWlJdIehvxu3lxycCI+8WjXa4/BKxccyyd7iY69a1CnCbtV13fnvwdTWMoF/49oGnt71psPyrpCCJXcjWiheAM4LvZ972nLSR26iYAtEbd2/5jqYBsn0bs+q8EvKBBnTCalDbzTSK/egKwCdEJqX3R02oVeFH9ofUG2zdKWgN4oe2HJG1MFG8/AHSbJp1gf2AWMQPhXbYXS9qUyKUvdRrdchVwnKQP2H4QoFpozyLiK+l44oT1SeAK29dV9XFHMnhqktLzLtt5NpSkdwKX215avT+cAdvz6oqrXXtxErF78Yvq7Q3ACVmc1FskbU0UzK7W8dIEyg8Wa2wf/yaStDcxeO2JZ/zklPpUtWlxBdEg43+Jh9/XEDn/O9v+fcHwWjVUqwPzbS+RtA3wmO0bS8aV+lsu/Buqmu76Ctv3NXXSq6TbgC/b/qqkY4ndnvUk7QCcZnuNEnGl0ZH0K2J0/HHAXztft/2b2oOieX38q7aZM6oWfCPuApdsm1kVaa8DtO4PE4hTuY1t718qrtT7JP3nP/q5tovuXldzBaYS942/EXVyP6xzknZbLC/smJEyrPa2yik9nzLVp6Hap7s2ddIrMe0yi5P6x2RgF9t3lg6kQ9P6+C9msLZm8UifWEqVMnA0kdqzEpG61cppLjbpO/WNf2p7f3miCPq3wI3E4nojoh7tv+sPbSjbfyNaZf6gdCzAI5Jeafs+4ndzpJ3XYiesqb/lwj89F1mc1F9uJRb/TVv4N6qPf9UW82nvd5JU8uF3f+AQ21+U9CfgzUSryrnE4iylUbP93tb7kk4HvmR7SOcvSccQaZ9p0HbAg23vZ8pFql0u/NNzkcVJPa6jfuRS4CxJs4mUn85pr6V2ilt9/DsfSIr38Zd0su2Pd7m+H9GF6J/rjwqI7j1zq/d/CbzF9rckHQqcBXy+UFyp/+xF7PB3Opv4v5cqtq9ue//HBUNJ41gu/NOo2T5d0o1EsdT86vJPgS2zOKlnfL/LtTldrg1Q7ui5yX38d5e0QitnXtK6wGlEV52Ssd1PPHT8gchpXp+YOnsPTy/eTum5WEjsXv+u4/o0oqA2dSHpKrrv+A8Q6VL3EAX6V9QaWOp7WdybUmq0qr3i9kRaz+rV5YVEEfJJtovdxCS9lpisfR2R+jYTuJKYyNm5EKozrlOBzYGPEB1NvgJMB3YD3m57nVKxpf5SdXc7gxj0dzPxcL458E7gvbZL9/JvJEmfI+5pc4n7B8BmxGyQc4jBaNOBA2z/V4kYU3/KHf+UUtPdRRSMfwu4Grja9iNlQwq275K0JbH43wuYbvv8wmFBPIC0Js6eRyz4W339318wrtRnbJ9b1ZHsTyxUIeqFtrb9s3KRNd62wEzbJ7dflPRTYE/b20r6MXAUkAv/9LzJHf+UUqNJejmx478D0ZZvBeByokvHfNsLa47nY10uvxg4gngwWbbDafvUuuJ6JpJeBCyx3TnNN6VUM0mPAet3ngxKej1wq+1J1RyC39oesfVnSs9G7vinlBrN9r3AudVba+jNwUR6AdRfe3DIMNcXAmp7fQAotvAfbugZkEPP0vNG0iTgo8DGwHJUE79bSs6yaDgDHwI+03F9b2LAGMSJXdEGBqn/5MI/pdRokiYSXUPe2vY2iUhduXqELx0Tttdsi+0Ntn9bdwzPpMvQs0nABsDpkmofepb62unArkSDh0WFY+klBwPfl/QOosXuROLhaW1gZ0mbAucDs8qFmPpRpvqklBpN0qPAisRC9krgGuDGJqSsSFoITLP989KxtJN0DXBp29Cz9at6hCOA3W1vUDjE1Cck3U9Msv5e6Vh6jaQ1gX2BNwFLgV8DX7P956pD2Jq2u3VeS2nUcsc/pdR0ZxO71lsR96wVgOUlXW/7iZKBEcN4VikcQzeNGnqW+tpSomVsepZs/x74tKQXA4tsP9n22m+A3xQLLvWtXPinlBrN9gGwbBLutsRDwMnAZEk32p5SLjquIo7rryQGjD3e/qLtQ4tE1eChZ6nvzAFmS5ph+/7SwfQKSROIeqBDiOYAa0k6EngE+ITtpSXjS/0rF/4ppV7xIDHUZiHwMHH/WrloRLAO0YN7ElGI165kHmWTh56l/rIHsB6wsEor+1v7i7ZfViSq5ptJFN1/HPhGde0i4GtED/+ZheJKfS4X/imlRpN0KDEZdEsireAK4Cyi1/X/FQwN29uW/P4juIgotDwWeGH18ULgGOCkgnGl/nNK6QB61Azgo7Yvk3Q6gO1LJD1OpDfmwj+NiVz4p5Sabi9gHtHd4tr2PNgmkPQm4FBgXaIzxwLgy7avLRhWY4eepf5i++zSMfSo1YFu073/SKT+pDQmcuGfUmo02xuWjmE4VSu+i4mBYhcRaTVbAFdLepftHxYK7ZUMDj07A1hBUrGhZ6m/SLqQ6OSzSNK3GCGtLfv4D+sm4H3AF6qPWz/DA4Cbi0SUxoVc+KeU0ujNAo6xfVT7xapt5tFAkYV/A4eepf6ymMGF6mPAk3QM7krP6JPAZZK2ITqVzZL0BqIof4eikaW+lgv/lFIavbWJ4sZOF1CwbWbThp6l/mK7vVXsdCKt7FLiROknTUvHa6jPAjsT9UuLiN/P+cBOtu8pGVjqb7nwTyml0fsj0TP/jo7rGwP31R/OMosYOvTseBoy9Cz1nfa0sm+SaWX/qDcD99j+XOlA0viSC/+UUhq9rwBfk/Qq4Prq2luAw4HjikXV7KFnqY9kWtmozQHOlTSHKMbvnAFyW5GoUt+bMDBQstV0Sin1LklrEl2HDgJWrS7/GZhtu3ibw46hZ1sAk4md/ykFw0p9ZIS0smuJblKzCobXWJKeGuHlAdv5wJTGRO74p5TS6N1J5DefDdxApDY0qW1mE4eepf6SaWWjs2bpANL4lDv+KaU0SpJezmB+81QipaZ4fvMwQ8/mA/NKDz1L/UXSV4gTpTWIdLerq7dMK0upgXLhn1JKz5O2/Ob3AZQ6rpf0C2Lo2TwaOPQs9Z9MK0upN2SqT0opjVJT22Y2eehZ6luZVpZSD8iFf0opjV7mN6dxbZi0srOAPTOtLKXmyYV/SimNXrbNTOPdXkRK2SwyrSylxssc/5RSeo4yvzmllFIvmFg6gJRS6gOZ35xSSqnxcsc/pZRGKdtmppRS6iWZ459SSqOX+c0ppZR6Ru74p5RSSimlNA5kjn9KKaWUUkrjQC78U0oppZRSGgdy4Z9SSimllNI4kAv/lFJKKaWUxoFc+KeUUkoppTQO/D9buuxySnx/3AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_corr.shape\n",
    "# 得到相关系数的绝对值，通常认为相关系数的绝对值大于0.5的特征为强相关\n",
    "data_corr = data_corr.abs()\n",
    "plt.subplots(figsize=(13, 9))\n",
    "sn.heatmap(data_corr,annot=True)\n",
    "\n",
    "# Mask unimportant features,突出重要信息\n",
    "sn.heatmap(data_corr, mask=data_corr < 0.5, cbar=False)\n",
    "\n",
    "#plt.savefig(\"house_coor.png\" )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weathersit and temp = 0.99\n",
      "casual and registered = 0.95\n",
      "instant and season = 0.87\n",
      "dteday and yr = 0.83\n",
      "windspeed and registered = 0.67\n",
      "instant and casual = 0.66\n",
      "temp and registered = 0.63\n",
      "instant and registered = 0.63\n",
      "weathersit and registered = 0.63\n",
      "season and casual = 0.59\n",
      "workingday and atemp = 0.59\n",
      "season and registered = 0.57\n",
      "temp and casual = 0.54\n",
      "temp and windspeed = 0.54\n",
      "weathersit and windspeed = 0.54\n",
      "weathersit and casual = 0.54\n",
      "weekday and windspeed = 0.52\n"
     ]
    }
   ],
   "source": [
    "#Set the threshold to select only highly correlated attributes\n",
    "threshold = 0.5\n",
    "# List of pairs along with correlation above threshold\n",
    "corr_list = []\n",
    "#size = data.shape[1]\n",
    "size = data_corr.shape[0]\n",
    "\n",
    "#Search for the highly correlated pairs\n",
    "for i in range(0, size): #for 'size' features\n",
    "    for j in range(i+1,size): #avoid repetition\n",
    "        if (data_corr.iloc[i,j] >= threshold and data_corr.iloc[i,j] < 1) or (data_corr.iloc[i,j] < 0 and data_corr.iloc[i,j] <= -threshold):\n",
    "            corr_list.append([data_corr.iloc[i,j],i,j]) #store correlation and columns index\n",
    "\n",
    "#Sort to show higher ones first            \n",
    "s_corr_list = sorted(corr_list,key=lambda x: -abs(x[0]))\n",
    "\n",
    "#Print correlations and column names\n",
    "for v,i,j in s_corr_list:\n",
    "    print (\"%s and %s = %.2f\" % (cols[i],cols[j],v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(train[\"weathersit\"], train[\"temp\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>season_3</th>\n",
       "      <th>season_4</th>\n",
       "      <th>mnth_1</th>\n",
       "      <th>mnth_2</th>\n",
       "      <th>mnth_3</th>\n",
       "      <th>mnth_4</th>\n",
       "      <th>mnth_5</th>\n",
       "      <th>mnth_6</th>\n",
       "      <th>...</th>\n",
       "      <th>weathersit_1</th>\n",
       "      <th>weathersit_2</th>\n",
       "      <th>weathersit_3</th>\n",
       "      <th>weekday_0</th>\n",
       "      <th>weekday_1</th>\n",
       "      <th>weekday_2</th>\n",
       "      <th>weekday_3</th>\n",
       "      <th>weekday_4</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>weekday_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   season_1  season_2  season_3  season_4  mnth_1  mnth_2  mnth_3  mnth_4  \\\n",
       "0         1         0         0         0       1       0       0       0   \n",
       "1         1         0         0         0       1       0       0       0   \n",
       "2         1         0         0         0       1       0       0       0   \n",
       "3         1         0         0         0       1       0       0       0   \n",
       "4         1         0         0         0       1       0       0       0   \n",
       "\n",
       "   mnth_5  mnth_6    ...      weathersit_1  weathersit_2  weathersit_3  \\\n",
       "0       0       0    ...                 0             1             0   \n",
       "1       0       0    ...                 0             1             0   \n",
       "2       0       0    ...                 1             0             0   \n",
       "3       0       0    ...                 1             0             0   \n",
       "4       0       0    ...                 1             0             0   \n",
       "\n",
       "   weekday_0  weekday_1  weekday_2  weekday_3  weekday_4  weekday_5  weekday_6  \n",
       "0          0          0          0          0          0          0          1  \n",
       "1          1          0          0          0          0          0          0  \n",
       "2          0          1          0          0          0          0          0  \n",
       "3          0          0          1          0          0          0          0  \n",
       "4          0          0          0          1          0          0          0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对类别型特征，观察其取值范围及直方图\n",
    "categorical_features = ['season','mnth','weathersit','weekday']\n",
    "\n",
    "#数据类型变为object，才能被get_dummies处理\n",
    "for col in categorical_features:\n",
    "    train[col] = train[col].astype('object')\n",
    "    \n",
    "X_train_cat = train[categorical_features]\n",
    "X_train_cat = pd.get_dummies(X_train_cat)\n",
    "X_train_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
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       "  </thead>\n",
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       "      <td>0.379232</td>\n",
       "      <td>0.360541</td>\n",
       "      <td>0.715771</td>\n",
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       "      <td>0.171000</td>\n",
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       "      <td>0.449638</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "       temp     atemp       hum  windspeed\n",
       "0  0.355170  0.373517  0.828620   0.284606\n",
       "1  0.379232  0.360541  0.715771   0.466215\n",
       "2  0.171000  0.144830  0.449638   0.465740\n",
       "3  0.175530  0.174649  0.607131   0.284297\n",
       "4  0.209120  0.197158  0.449313   0.339143"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数值型变量预处理，\n",
    "#感觉数据已经做过处理（取值都在0-1之间），这里用MinMaxScaler再处理一次\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "mn_X = MinMaxScaler()\n",
    "numerical_features = ['temp','atemp','hum','windspeed']\n",
    "temp = mn_X.fit_transform(train[numerical_features])\n",
    "\n",
    "X_train_num = pd.DataFrame(data=temp, columns=numerical_features, index =train.index)\n",
    "X_train_num.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>season_3</th>\n",
       "      <th>season_4</th>\n",
       "      <th>mnth_1</th>\n",
       "      <th>mnth_2</th>\n",
       "      <th>mnth_3</th>\n",
       "      <th>mnth_4</th>\n",
       "      <th>mnth_5</th>\n",
       "      <th>mnth_6</th>\n",
       "      <th>...</th>\n",
       "      <th>weekday_3</th>\n",
       "      <th>weekday_4</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>weekday_6</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.607131</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   season_1  season_2  season_3  season_4  mnth_1  mnth_2  mnth_3  mnth_4  \\\n",
       "0         1         0         0         0       1       0       0       0   \n",
       "1         1         0         0         0       1       0       0       0   \n",
       "2         1         0         0         0       1       0       0       0   \n",
       "3         1         0         0         0       1       0       0       0   \n",
       "4         1         0         0         0       1       0       0       0   \n",
       "\n",
       "   mnth_5  mnth_6     ...      weekday_3  weekday_4  weekday_5  weekday_6  \\\n",
       "0       0       0     ...              0          0          0          1   \n",
       "1       0       0     ...              0          0          0          0   \n",
       "2       0       0     ...              0          0          0          0   \n",
       "3       0       0     ...              0          0          0          0   \n",
       "4       0       0     ...              1          0          0          0   \n",
       "\n",
       "       temp     atemp       hum  windspeed  holiday  workingday  \n",
       "0  0.355170  0.373517  0.828620   0.284606        0           0  \n",
       "1  0.379232  0.360541  0.715771   0.466215        0           0  \n",
       "2  0.171000  0.144830  0.449638   0.465740        0           1  \n",
       "3  0.175530  0.174649  0.607131   0.284297        0           1  \n",
       "4  0.209120  0.197158  0.449313   0.339143        0           1  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Join categorical and numerical features\n",
    "X_train = pd.concat([X_train_cat, X_train_num, train['holiday'],  train['workingday']], axis = 1, ignore_index=False)\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant  season_1  season_2  season_3  season_4  mnth_1  mnth_2  mnth_3  \\\n",
       "0        1         1         0         0         0       1       0       0   \n",
       "1        2         1         0         0         0       1       0       0   \n",
       "2        3         1         0         0         0       1       0       0   \n",
       "3        4         1         0         0         0       1       0       0   \n",
       "4        5         1         0         0         0       1       0       0   \n",
       "\n",
       "   mnth_4  mnth_5  ...   weekday_5  weekday_6      temp     atemp       hum  \\\n",
       "0       0       0  ...           0          1  0.355170  0.373517  0.828620   \n",
       "1       0       0  ...           0          0  0.379232  0.360541  0.715771   \n",
       "2       0       0  ...           0          0  0.171000  0.144830  0.449638   \n",
       "3       0       0  ...           0          0  0.175530  0.174649  0.607131   \n",
       "4       0       0  ...           0          0  0.209120  0.197158  0.449313   \n",
       "\n",
       "   windspeed  holiday  workingday  yr   cnt  \n",
       "0   0.284606        0           0   0   985  \n",
       "1   0.466215        0           0   0   801  \n",
       "2   0.465740        0           1   0  1349  \n",
       "3   0.284297        0           1   0  1562  \n",
       "4   0.339143        0           1   0  1600  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FE_train = pd.concat([train['instant'], X_train,  train['yr'],train['cnt']], axis = 1)\n",
    "FE_train.to_csv('FE_day.csv', index=False)\n",
    "FE_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 35 columns):\n",
      "instant         731 non-null int64\n",
      "season_1        731 non-null uint8\n",
      "season_2        731 non-null uint8\n",
      "season_3        731 non-null uint8\n",
      "season_4        731 non-null uint8\n",
      "mnth_1          731 non-null uint8\n",
      "mnth_2          731 non-null uint8\n",
      "mnth_3          731 non-null uint8\n",
      "mnth_4          731 non-null uint8\n",
      "mnth_5          731 non-null uint8\n",
      "mnth_6          731 non-null uint8\n",
      "mnth_7          731 non-null uint8\n",
      "mnth_8          731 non-null uint8\n",
      "mnth_9          731 non-null uint8\n",
      "mnth_10         731 non-null uint8\n",
      "mnth_11         731 non-null uint8\n",
      "mnth_12         731 non-null uint8\n",
      "weathersit_1    731 non-null uint8\n",
      "weathersit_2    731 non-null uint8\n",
      "weathersit_3    731 non-null uint8\n",
      "weekday_0       731 non-null uint8\n",
      "weekday_1       731 non-null uint8\n",
      "weekday_2       731 non-null uint8\n",
      "weekday_3       731 non-null uint8\n",
      "weekday_4       731 non-null uint8\n",
      "weekday_5       731 non-null uint8\n",
      "weekday_6       731 non-null uint8\n",
      "temp            731 non-null float64\n",
      "atemp           731 non-null float64\n",
      "hum             731 non-null float64\n",
      "windspeed       731 non-null float64\n",
      "holiday         731 non-null int64\n",
      "workingday      731 non-null int64\n",
      "yr              731 non-null int64\n",
      "cnt             731 non-null int64\n",
      "dtypes: float64(4), int64(5), uint8(26)\n",
      "memory usage: 70.0 KB\n"
     ]
    }
   ],
   "source": [
    "FE_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 从原始数据中分离输入特征x和输出y\n",
    "y = FE_train[\"cnt\"]\n",
    "\n",
    "X = FE_train.drop([\"cnt\"],axis=1)\n",
    "\n",
    "#特征名称，用于后续显示权重系数对应的特征\n",
    "feat_names = X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(584, 34)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将数据分割训练数据与测试数据\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 随机采样20%的数据构建测试样本，其余作为训练样本\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33, test_size=0.2)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>yr</td>\n",
       "      <td>4550.708897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>temp</td>\n",
       "      <td>2654.792827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>mnth_9</td>\n",
       "      <td>1287.992360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>atemp</td>\n",
       "      <td>995.293778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>mnth_10</td>\n",
       "      <td>929.887649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>weathersit_1</td>\n",
       "      <td>914.410909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>season_4</td>\n",
       "      <td>830.579518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>mnth_12</td>\n",
       "      <td>586.296405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>mnth_8</td>\n",
       "      <td>517.803038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>weathersit_2</td>\n",
       "      <td>409.589079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>mnth_11</td>\n",
       "      <td>407.138803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>weekday_6</td>\n",
       "      <td>238.417312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>workingday</td>\n",
       "      <td>216.956549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>mnth_6</td>\n",
       "      <td>189.797216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>season_2</td>\n",
       "      <td>85.141889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>weekday_5</td>\n",
       "      <td>77.516263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>weekday_3</td>\n",
       "      <td>66.464787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>weekday_4</td>\n",
       "      <td>43.650546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>mnth_5</td>\n",
       "      <td>5.009200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>instant</td>\n",
       "      <td>-6.919060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>weekday_2</td>\n",
       "      <td>-31.943212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>season_3</td>\n",
       "      <td>-130.807162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>weekday_0</td>\n",
       "      <td>-191.612446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>weekday_1</td>\n",
       "      <td>-202.493250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>mnth_7</td>\n",
       "      <td>-203.137582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>holiday</td>\n",
       "      <td>-263.761415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>mnth_4</td>\n",
       "      <td>-485.714239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mnth_3</td>\n",
       "      <td>-541.439917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>season_1</td>\n",
       "      <td>-784.914245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>-1174.845790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>mnth_2</td>\n",
       "      <td>-1207.111892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>hum</td>\n",
       "      <td>-1298.592138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>weathersit_3</td>\n",
       "      <td>-1323.999988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>mnth_1</td>\n",
       "      <td>-1486.521042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         columns         coef\n",
       "33            yr  4550.708897\n",
       "27          temp  2654.792827\n",
       "13        mnth_9  1287.992360\n",
       "28         atemp   995.293778\n",
       "14       mnth_10   929.887649\n",
       "17  weathersit_1   914.410909\n",
       "4       season_4   830.579518\n",
       "16       mnth_12   586.296405\n",
       "12        mnth_8   517.803038\n",
       "18  weathersit_2   409.589079\n",
       "15       mnth_11   407.138803\n",
       "26     weekday_6   238.417312\n",
       "32    workingday   216.956549\n",
       "10        mnth_6   189.797216\n",
       "2       season_2    85.141889\n",
       "25     weekday_5    77.516263\n",
       "23     weekday_3    66.464787\n",
       "24     weekday_4    43.650546\n",
       "9         mnth_5     5.009200\n",
       "0        instant    -6.919060\n",
       "22     weekday_2   -31.943212\n",
       "3       season_3  -130.807162\n",
       "20     weekday_0  -191.612446\n",
       "21     weekday_1  -202.493250\n",
       "11        mnth_7  -203.137582\n",
       "31       holiday  -263.761415\n",
       "8         mnth_4  -485.714239\n",
       "7         mnth_3  -541.439917\n",
       "1       season_1  -784.914245\n",
       "30     windspeed -1174.845790\n",
       "6         mnth_2 -1207.111892\n",
       "29           hum -1298.592138\n",
       "19  weathersit_3 -1323.999988\n",
       "5         mnth_1 -1486.521042"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 线性回归\n",
    "#class sklearn.linear_model.LinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=1)\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# 1.使用默认配置初始化学习器实例\n",
    "lr = LinearRegression()\n",
    "\n",
    "# 2.用训练数据训练模型参数\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "# 3. 用训练好的模型对测试集进行预测\n",
    "y_test_pred_lr = lr.predict(X_test)\n",
    "y_train_pred_lr = lr.predict(X_train)\n",
    "\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(feat_names), \"coef\":list((lr.coef_.T))})\n",
    "fs.sort_values(by=['coef'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LinearRegression on test is 0.8279474225980328\n",
      "The r2 score of LinearRegression on train is 0.8516480637403496\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score  #评价回归预测模型的性能\n",
    "# 使用r2_score评价模型在测试集和训练集上的性能，并输出评估结果\n",
    "#测试集\n",
    "print (\"The r2 score of LinearRegression on test is\", r2_score(y_test, y_test_pred_lr))\n",
    "#训练集\n",
    "print(\"The r2 score of LinearRegression on train is\", r2_score(y_train, y_train_pred_lr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#在训练集上观察预测残差的分布，看是否符合模型假设：噪声为0均值的高斯噪声\n",
    "f, ax = plt.subplots(figsize=(7, 5)) \n",
    "f.tight_layout() \n",
    "ax.hist(y_train - y_train_pred_lr, bins=40, label='Residuals Linear', color='b', alpha=.5); \n",
    "ax.set_title(\"Histogram of Residuals\") \n",
    "ax.legend(loc='best');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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prvWc0CkWd/3AeycV5yHjChIzwRRjn71G8NPj96OsKK/f6T7TEc/mdlWtAxb158VEZA7wRlBzi6qOEJFsTFDl2Y5cDwFXqGp7wP3zgR8BBZja0xerakNA//mYetTjgJeAC22kd28iKYXNTbuPoKP5ScRaLP6SY8o5ZFpxz+rp443xLTSfqvzv7LJe1wPpRZsKhFUwIrIVqFDVWhHZRgTvbFXdy+PrVWCOt78S0OZu3G/CbLe+CgwHHgTaMdszROQiTAT3ORiP4j8A9wOnO/0nYiK7v4dZYd0GPCkiB1tnQIOXhNfBofphEzrFUCzefW5nV3dMW6p0J3dYJsfPGN+rbSC9aFOBSCuYnwJNzv+viNPrVQJVqro5sFFEcoCLgLNU9U2nbR7wiIhco6ptGIPyDar6vNN/LvChiExyIrsvB+51j9JF5GxgE/AlYHGc5E9bllTXcsO/Po6ajsBr7WEv5UMC2b9sNL958dMho1wADpxcFPL3mEgv2v7EjiWCsApGVR8MuDwUuF1VV/fz9SrZc4sEMBPIB14LaHvVaZspImuBaYH9qrpCRLYDh4vIBkfGhQH9dSLyEVbBxGSM9Wpk9HLa5DIqdxglo3Jj2lKlOxkZMHffsRHHxFOx9Cd2LJF4tcGcBfw6Dq9XCTSLyHKgGKNELscETraoas8nUFUbRaQVk+TKtcN8HvS8TU5/ISYIM1y/Z3bu3Bm2z+/39/qZLix8cZXnlUNZYQ5j8zIj/h5cfvClSazZ2szmpvDPzsyAC4+YzOot24fMqRGYiO+XVm7m2weXxv3ZwZ/Dt9bUc9WTH/d6H9z4r9VbmrnltAoOnVYUdzm84FXB/AG4U0RuAtYQlBtGVaNmCxKRUUApMAw4H7M6uQmTCmIBEOpT6sdk0ssLuO5Lv2eqqqqijqmuro7lkUllc3MHy2u8505p27nT0+8AjKX9wll5/O2Tbj6pbWdXkBvMiGEZHLd3Lr6ddaxfV0duNrQNAh1TMiKT5vYuouWmWlbTwEvvLGdcfmJCF9zP4YJX69kcRpjNTX4WPFfFtUeltoL5LmbF8dUw/VGDS1R1h6NkWlS1E0BETgM2YhRWKEXgA1rZrdB87LYLhesPdb9nKisrw/b5/X6qq6spLy/H54tJbyWNxjV1tHXUeh6/ra2bUaXT9ojU3bC9jZrtrZQV5vXqq6yEM48x/Ru2t1HX0s6WRj9ZGfDyp3U8v7aRv33SSu6wTLKysqAj1RMjRKdpVwZnf3ESf3hjfcRxbR3d5I0pozLOq4fAz+HWli7W7tgWcfzaHZ0h39N4Eu6PklcFc3o8hFDVxqDrLSJSB5QD+SJSoKpN0JNo3N32uEfNJUDgt6XE6a/DKJKSoJcsobddJyo5OTlRx/h8Pk/jUoG9xxXGZIxt9neytaWT8hIzP697+/KSnJ7cJCGLywcvb9KYZn8n+00oZIRvY1Snub3Hj07YZ8Xn87F1WzPNEeodwZ7v6UDi1dHuVVV9FVgKNAM7gHcC2qMiIoeKSJOITApomwSMAd4EWoAjAm45ymlb7pw6rQnsF5EZwGjgTecY+u2g/mJgOrDEi3yDFS/OXYEEGnlDOeW5e/ufPPEBS6qNrg/OqxuLA15WBuQOS6/IpAJfNgdNKUoJp7mBzrEbK17LlviAe4BvYnxUANpE5D7gcnfLE4UPMNuhP4nIT4AczKnPK6r6qvOsu53j5wzgt8AiVXWtjQuBX4nIOmAzcB/whHNEDfAb4FERWQEsw6SX+EBVh/QJEkR27gom8EsRLbfLTc+uZHh2Jis3NtLW0dWTV3fFBu/F5Tu7YVea5W5xf0ep4DQ30Dl2Y8VrNPVCYC7wDcyJTxnwbYxN5iYvD1BVP8aRrhlzevQfYBW7t19XOW3PAH/HeOrOD5Lhd8CfMXFRHwPfD3j+0xjfnZsxK6J24DSP8xvUBLr+52aHf8vHF+z+Unjx1P1oYyPvr2+gzQlydPPqxlr5I530S6Di6EuN7EQw7+h9KBkV2iaYbO/gjO7u6O+u429ysqq+HtQ+F3g8Bk/elGbp0qXds2fPDtu/0zlhqaysTBsbTDA19a38+6NN/KdqC7q5qXew4wmVzN3PHKu+UV3LN+9/O8nSJo8ROZlMKRrBurpWT962iU49GUioz+GS6tqkegcvXbqU2bNn77HX9WrkbQNCZfEZOp5Tg4Syojx+cOTe/ODIvXu+FHvlZ7Fj4xoqp+4+7YjVU3cwkZUJU4pGMP/ECiYW5XpSHAOhWCIxUDl2Y8XrFulq4H4RmSMimQAiMh1jl7lRRPLcf4kS1BJ/yorymFM+JuTxZazG4cFEZ5fZ/v3kiQ+oqW/tyT/clwJxA437nqaCcgHvK5i7MMXXFgOdItKFcZjLAA7G1Kl2SUzCVcuAE4txeDDiGrJH5g5LORf8dMGrgjk1oVJYUpJQkb/5w7No29WZFkXK4kHVxsZeaQS8lm+xGLzWRfLk62IZfATv7bc27eQX/6iicTD4/HsgnB6NVr7FYrB1pS2ecPf0v37uk0GhXGSvEZw8o4ScCMf20QhXvsWym/QvIGOJGa85Q4LHxeKhm8pkALq1Gd3a3K/neM2dM5SxCmYIES6u6PuHTyIwQ+5TH2zgvsVr+ayuhRZ/p/HQHVdAVZqnuRyWCbu6IqRmDCKDyGMHW4LuRGAVzBAhUs3ojzc2cs70XJpy67n1v9Ws3NS0h2Fz6XrvKR9SkcrSkayva2FXlMBAoMdJraHFT9WmprDjBluC7kQQKSfvy3hU9qp6TNwksiSESNub7a27+M07uxj23jJ2DcLjofEFPk6bOYEbnl0Zdew1J+3HVyrHU1aUx5tOUOdQSdCdCCJZuN7DRE8vxZQqOQqTwGkxJhZoO3A4sDzBMlr6idcKAINRuUwdnc0tX6ug00NIDEBXV3fPqiRVYo3SmUg5eX/q/l9E/g5craq3Bo4RkYuxAYUpT6wVAAYD2ZkZnD9nMseW7KRyahE5w1s83bd/2ehe16nqgp8ueD2jOw54MkT788Bh8RNnaBOcVyVe93vJGZLu5PuMA3ludiazJxfy0HmHcNn/27un/9C9ixmZG/l3MCp3GIdMC70qSTUX/HTB66duNXAmcH1Q+/cwaROGDFtaOmhcU0f5+MKUyQgf7f5YKgCkK18YX8DxFSUcP2N8z/sSnLj8kqP34aZ/rwzphZyZAfOOtjaVeONVwfwMeFpE/gd4n90xSPtgVjeDniXVtSx8cRXLaxpo66iNW0xKpNOdYHf0UP4rXu8f7HFFS9c1sLGhjcoJI8Mq/vOPnEYX3Sx6eTU72nYnBxiVO4x5R5dz/pHTBkrcIYPXUIHnnBSV38OUHgFTDeA0VV2XKOFShViUQKxEyxq36JXVPeNCrVC83D+nfAwTi3I59YAJ/P61NZ79QNINL+77bqqKt9fU8WFNA/uXjQ67LbL0n1hqU68CrhSRQqDRY5rMQYHXL3GseDndeX9dPZc+1si2gLIUrnJbtaWZhpbIK5L3P6vn6/csYdWWpp68uoOZ4NK34ThkWrFVLAOA15y8GZh0lD/FFDnbV0R+iSkh8mNVDZWMalDgRQl4/VAH4+V0p21XF227Qte82Rah4FnP/R1dae8kFwvWfT+18HqKdAXwQ0zxefdT/VfMEfXNCZArZfCiBNwPdawMhdOdgca676cWXhXM+cCFqvoI0AWgqs8A52LKyg5aElkWYihnjUsU1n0/tfCqYCYBn4ZoX4/ZMg1avCiB/nyoI2WEH1vg61c6gaGGdd9PPbx+epdi/GBc3IOIH2GOrQc1iSwLEckdfeE3ZlFROrLPzx4q5GRnWPf9FMWrAeBy4DkROQpT7/lGEfkCQ8QPxlUCv31xFctqGmjr6I5rWYhQ7ug19a389uVPWbmpMfoDwjA8K5P2zsFTsjUU00tH8rtvzbbbohTFqx/M2yKyL2bF0gjkYvxgTlbVzxMoX8owp3wMB04cwUvvLCdvTBl7j4//Xt+NcwnldxMr2ZkZdDO4lUvJKB/zT6ywyiWF8XpM/QvgdlW9Nqh9pIjcoao/SYRwqci4/GwqpxXFpfBauMxy/c0clwGM8GXRMAhSW7oMz8ogKyOjp0TtQBYVs/SdSPlgJgCudfOXwEsiUh80bCZwIeBJwYjIROBO4GigA/gXprZ1g4icDTwcdEuVqk537h0JLAJOBnZiajJd7xS+R0SygQXA2c68HgKuUNXQTiRJJFLs0ITCXE+pFSLRDYNGueT7sphZVsjFc8s9F0GzpA6RVjBfxNSIdg26r4UZ90cvLyQiWZh601sxCiYXU2v6QeAUoAJ4FhOO4BLowHc/pib2XOfng0A9cLfTfxOm9vVXgeFOfzvGhydliBZ28N05U4dcaoVI3HjqdE6dNbHn2iqW9CJSPpinRGQK5qRpDSa4cVvAkG6gWVWDVzXhmAUcCJSo6mYAEbkEeF1ERmNinD50+wIRkcnA6cABqroCWOZ4Ev8YuFtEcoCLgLNU9U3nnnnAIyJyjaq2eZQx4UQLO3j+4y1pVbI1MwNyh2XREmvFew8U+LKZPbko+kBLyhLxmFpV16vqZ6qaCTQDxaq6zglwPBWIJQBnLXBCkAJxV0c5GAXzSZh7DwN2OMrF5VVgmoiUYLZq+fReZb3qtM2MQcaE4iXsYFnNdkblDhsgifrPz0/cj/vP+WLYY/xQjC0YzvwT9uPASaMjjrNOc+mPVyPvV4HHgF+x2+/lJOBmETlFVV+I9gxVrcOcPAXyY0w6zgZgGnCsiFyNUTjPAleqahMwAQg+rdrk/Jzo9Leoas+3V1UbRaTV6fdMcA6RQPx+f6+fsbJ6y/aoK5POLvi8IWUWXFGZVpzDrIn53HxKBfcu/owVGxtp9neSOyyTiaNzGJaVyfrtbTT7Oxnhy2JG6UguOHIKh04tYp+9crjq7x+zOURM1fgCH98/vCzi+xGN/r5fqUo6zcurH8yNmC/7QrdBVY9ztji3YrY+MSEiVwJfxxhtBVPTuh3j0FeKMdg+ilFkeeyOgXJxr31h+t0x3v+0AlVVVVHHVFdXx/LIHlqbO8jNhkFifyU3O4PW2hqq2jZRAFxxkI8tLYVsbelkr/wsxuWbj9eWlpzeba2bqKoy91w4K4+/fdJNdf0u2jq6yc3OoLxoGF/fL48CZ1x/6ev7leqkw7y8Kpi9gX+GaP8ncEusLyoi12BWQ5eq6r+ctiJVdcN+l4nINuAdESkH2thTUbjXrWH63TExRSFWVlaG7fP7/VRXV1NeXo7PF5PeMs8GDvjkfd5aOziim2eWjeaYgw/o1Rbqtxf+NwqVlXDmMbBhexsbtrcxsTCXiYW5cZGvv+9XqpKK8wr3h9mrglmFOem5M6j9BCCmhFMichcmKvsiVf2d2x6gXFxciScCG4CSoH73eiPm1ChfRAqcLZV7rJ3HnluriHjxb/H5fH32g7n0y8KqR5ZS35LeGS5KRvm45MsSF38ggPKSHMpLEhPW1p/3K5VJh3l5VTA3AI+KyJeAdzHG2QMx6RrO8fpiIvIrYB5wrqo+FNB+BsavpUxV3U33bEzk9irMKVaRiFSoqpsD+ChgjapuFpEdQAtwBMZ24/a3kGJlVQ4rL+YXJ1Vw2eMpJZZnrJObJRa8hgr8VUS+gnGq+xbGVrIKOFJV3/LyDBGZBcwHbgf+KyLjA7pfwSitP4nIdRgbzL3Ag6q60bn/KeAvInIBZvVynfM8VLVNRO7DHFmfi3Fm/S2wKEBhpQwHTi4iM4OQyadTlbEFw/nBl/bulVTbYolGLCkzXwJe6sdrfR1zLP4z518gMzBBk7djCr7tBB4JGnce8HvMUXQjcEfgFgu4CuO89wzGS/hhHAWUatRsb0155ZKbnWnd8i39JlKowOPA+c5x7+ORHqKq/xvthVT158DPowz7coT7twPfiNDvx6ywLowmS7Jxk1hFOrLOyoCc4Vk9xeeLRwzns7q+1UuKlQJfNvefexCdTpVDu2Kx9JVIK5gWdjvCeSuLZ/GElzpFh+49hlu+NqMn9qa7G05cuHhAPHz3LxtN6ehc1vexAJzF4hIpVOC7of5viQ+R6hS5SayCVw8yriDhCbyL8ofR0OLnxN8s7lMROIslkEhbJM+nQ6r6l/iIM3Q4rLyY7x4+lUXNHG4yAAAQMklEQVQvV7MjwPNuVG423zt8Wq8v85LqWm7/j7J8fUNCZcrOzKC9o5OqTU09bfGq/2QZmkTaIi0Iui7CHBtvxJwilWG8bz8FrIKJkSXVtTzwxtpeygVgR1sHf3xjDRWlI5lTPoY/vLaa259X2jsTbxXu6Oqm2R/6dfpT/8kydIm0RRrr/l9EfgR8DTjHzWAnImOABxgCOXkTwQ3/+thTRcfbnld2DYBy8UJf6z9Zhi5ek37/EuPW3+MVq6q1mKPhSxIh2GDmqfc3sDJgGxKKD2sauPHZlSmjXKDv9Z8sQ5dYamIEu+qDiVFKOUe2VOe+19dGrQ/d5O/gk819T/idCGxRM0useHW0ewB40HH1/wDjKXsIxpHtrgTJNmgIzL3b3Q1ra72d+qdaQQCbn8USK14VzNWYiOVfAuOctk3Arap6eyIEGwyEyr07qTiP1gRkf0s0RfnZtqiZJWa8xiJ1AdcC1zrGXdcGYwlDuNy7VRsbyYCoW6R4Eo/XKx2VZ/1gLDHj2QYjIpNF5FZM8u1hIvIdETk0caKlN5Fy7w602TYer7eurtUaeC0x40nBiMghmPwsB2BywORict2+JiInJ0689MRL7t2Bpr8Vru0JkqUveP3c3QbcoKrHYZzsUNXLMFnprk+QbGlLzfbWlKsKEM1enBGl354gWfqCVwUzC3giRPvDwL7xE2dw4EZLpzq+7AwqS0dy29cqmL7X8Ihj7QmSpS94/RbUYQrdrw5q/yKwJa4SDQK8REunAtMnjOZvP5zDzp07aanbxLa2jJAZ/t3gS4slVryuYO4G7hWRszCr6ZlORYF7MJnnLEHMO3ofhmVF23gkl1Wbm3rsKjP28nHLaRUcXj6GAmf1VeDL5vDyMdxxxix7gmTpE16PqW8XkSbgZkwi7b8CmzG5en+TOPHSlwmFuWRlZLBrwM+MvOMabsfmjQDg0GlFzK0opaa+1daAtsQFr4XXvg38VVXvFZF8IEtVU8uPPcWo2d7Kzo4Uc8UNIpzh1ioWS7zwaoNZCLwD1KmqzW4XhZr6Vhav2hZ9YJJxDbf9qZ5osUTCq4J5G1OiJOYia0OJ4NCAgWZ66UjOP2IqTyz9nA9rGmjyd4T14rWGW8tA4FXBdAE3icjPMUXsexVPVtWD4y1YuhEqNGAgKRnlY/6JFRxWXsypsyb22FG2Nu3spXBslQDLQBLLCubtRAqS7kQKDUg0o3KH7XHSE2hHCVQ41r5iGUi8niJdl2hB0plkhwZ0dXUzsShyPWerWCzJIFLS7zxMrpfTAT/wJHCVPT3ak0SGBmQS3c3fPW62CsSSakRawVwHnAzcCnQCFwPFRCh+NlTxUkgtmOxMGJaVSduuyOrDNyyTjIyMiDlkbJyQJVWJ5Ml7OnC2qt6iqrcBZwCniMiwgREtfXBDA6KROyyTGaUjmX/Cftx+xgHkDovuSN22q4upxZGVh40TsqQqkVYwE4FPAq7fxSikccCGRArVV0QkG1Nu5WzM3B4CrlDV9kS/dqRCamMLfPzgiKkcP72kRxGcfd9b1LdGX/EU+LL5wZF7c8tzKyMWabNYUpFICiYLszUCQFW7RcQPRA67TS43YfLVfBUj54OY9BJXJPqFDysvZsEZM1n0yuqoR8KxGIX3LxvNKbMmMLbA5+nZFksqkfo5BTwiIjnARcBZqvqm0zYPeERErlHVtogPiANzyscwp3xM1CNhr0bhovxhPasTr8+2WFKJaArmOyLSHDT+WyLSKx+vqt4Td8liZyaQD7wW0Paq0zYTeLO/L9Dc3Bx9ENGPhL0YhbMy4JcnV+6xOrGKxZJORFIw64EfBrVtBr4b1NaNSduQbCYALaras/dQ1UYRacXYkzwRKS6nrq6O2tpa/P7+OdTtlZ/J9NIC3lobvpD9wVMKOe4LxQMSJ+TOp7/zSjXsvJJPpNKxUwZQjniQh/HXCcYP+Lw+pKqqKmJ/e3s71dXVsUkWguMnwaebM6lr2/OYujg3k+MmRZcl3sRjXqmInVfyGDQ2GEx8VChF4gM8Z6uurKwM2+f+xSgvL8fn86yzwrwOTJlcz72LP2PFxkaa/Z2M8GUxo3QkFxw5hUOnFvXr+bHg9/uprq6Oy7xSCTuvgSPcH8PBpGA2APkiUqCqTQAiMhKzsvk84p0B5OTkRB3j8/k8jYvG3IrSlErwFK95pRp2XsljMCmY5UALcATwrNN2lNO2PFlCeSHZisViSRSDRsGoapuI3AfcLSLnYnIH/xZYpKo2o5LFkgQGjYJxuApTFO4ZoANTVmV+UiWyWIYwg0rBqKofuND5Z7FYkkx/K4paLBZLWKyCsVgsCcMqGIvFkjCsgrFYLAnDKhiLxZIwrIKxWCwJwyoYi8WSMKyCsVgsCcMqGIvFkjCsgkkQNfWtvFFdS02950wRFsugY1CFCqQCS6prufvlalZs2BGQnHsUF8/dxybntgw5rIKJI0uqa7n8iWW9yos0+Tt4o7qONduaWXDGTOaUj0mihBbLwGK3SHHk7perQ9YuAti0w8+iV1YPsEQWS3KxCiZOeKl19GFNg7XJWIYUVsHECS+1jtwi9RbLUMEqmDjh1jqKhC1SbxlqWAUTJyYV57H/xFERx9gi9ZahhlUwcWTe0ftQMip0GQlbpN4yFLEKJo4cVl7MgjNmcnj5GAqc7VKBL5vDy8dwxxmzrB+MZchh/WDijC1Sb7HsxiqYBGEVi8UCGd3d3cmWIWVYunSp/WVYLH1k9uzZGcFtVsFYLJaEYY28FoslYVgFY7FYEoZVMBaLJWFYBWOxWBKGVTAWiyVhWAVjsVgShlUwFoslYVgFY7FYEoYNFfCIiGQDC4CzMb+3h4ArVLU9qYIFICITgTuBo4EO4F/A5araICJnAw8H3VKlqtOde0cCi4CTgZ3APcD1qtrt9Cdt/iIyB3gjqLlFVUd4kUtE5gM/AgqAp4GLVbUhoP98YD4wDngJuFBVNyRwSu7rfgd4IEz3UcBE0vQ9c7EKxjs3AScAXwWGAw8C7cAVyRTKRUSyMF+erRgFkwv8DiPnKUAF8CzwvYDbdgX8/36gDJjr/HwQqAfudvqTOf8KYAXwlYC2Li9yichFwCXAOUAd8AfMXE93+k8E7sL8XlYCtwFPisjB7hc1gTwGPBfU9gAwGlgCXEv6vmeADRXwhIjkALXAWar6jNN2MvAIMFZV25IpnyPPQcC7QImqbnbaDgdeBwoxH9xPVPXqEPdOBtYCB6jqCqdtHnCZqu6d7PmLyJ3AeFU9K6g9qlwishq4S1V/6/TPAD4EJqvqehF5EVimqpc7/cXAJuDLqro4kfMKRkROBR4FKlV1tYg8SZq+Zy7WBuONmUA+8FpA26tO28ykSLQna4ETXOXi4P71yAEqgU/C3HsYsMP9oDq8CkwTkRKSP/9wskeUS0TGA9MC+505bgcOF5FM4NCg/jrgI+BLcZ5DRJztzC0YZeiWn0jn9wywWySvTMDs+XvKBqhqo4i0YvbJScf5YgQvt38MVAMNmC/asSJyNUbhPAtcqapNmPl9HnTvJufnRJI//0qgWUSWA8WYL8rlHuRybQ2h5jYRs7LLi9A/kJyO2ebcCj2rs3R+zwC7gvFKHhCq4JEfCJ0jM8mIyJXA14HLAAGyMF+4M4GLMHaaR53hoebnXvvC9LtjEjp/ERkFlALDgPOBbwFTMco0mlx5Add96R9ILgL+rKr1znXavmeB2BWMN9oI/ab4gJSrQyIi1wC/Ai5V1X85bUWqut0ZskxEtgHviEg5oefnXreG6XfHJHT+qrrDUTItqtoJICKnARs9yNUWcN0UpT/U/QOCiJQCRxBgfFXV5en6ngViVzDe2ADki0iB2+AcEYZaXicVEbkLuA64SFUXuu0BH1SXKufnRMz8SoL63euNJHn+qtroKhfnegvmRKg8ilzuUXOouX3uPKM1Qv9AcSJG1ncDG9P5PXOxCsYby4EWzF8Zl6OctuVJkSgEIvIrYB5wrqr+LqD9DBHZ5uzrXWZjjnpXYY5Ei0SkIqD/KGCNYzRO2vxF5FARaRKRSQFtk4AxwJuR5HJkXxPY75wijQbedI6h3w7qLwamY34nA8VhwGuBx+Lp/J4FYo+pPeIclZ4CnAtkAH8BHlPVK5MqmIOIzALeA27HONsF0on56/cCZnVTCtwLvK6q5zn3P4kxMl6A+Uv4F2C+q6iSNX8R8WGOlWuAn2CMnQuBVlU9JppcInIpcI3Tvxm4D/hUVb/h9J+CsWv8AFiG+f2NVtVDEjmvoDm+C/xdVW8OaBtLmr5ngVgbjHeuwjivPYPxkn0Y4/2ZKnwdsyL9mfMvkBnAcZgvz3sYr89HgsadB/wec6zZCNwRuAoiSfNXVb+InADcgTk9ygD+gTFee5FrIWa182eMofifGIOp+/ynReSnwM2Ylc3LwGmJm1FIxmEc5HpQ1W0ikpbvWSB2BWOxWBKGtcFYLJaEYRWMxWJJGFbBWCyWhGEVjMViSRhWwVgsloRhFYzFYkkY1g/G4gkR+TPGYSsc16nqtQMjTWw4so9Q1dOTLctQwyoYi1cuxThugYn0fQU4GONhC9CcBJm8cinGQc8ywFhHO0vMiMh0TArLqar6WZLFsaQwdgVjiRtOEutLgI8xeWBvxUQ899qeiMhnwO2qerdzfQ7wc0yU8CfANW6aiRCvcS1wICaD33nADudZdwX0H4KxLx7qyHN0oAwicjomPmlfTDDk/6nq007fIZiwhNmY1dl9zvPdHMCWGLBGXku8mYXJvXIgJrguIk68zULgF5iYqXuBv4rIYRFuOw6YhFEgVwM3i0igfeh4THzOoZgscIGvdwwm2fZDzuv9AXhcRCpEZC/geUwyqxkY5fQj9oztsnjErmAsieC6gMTj0cb+H3CbqrqZ2laLyGxMSsxwRtk24BwndWSVM/4iTNZ8t/9md9URJMMPgX+o6u3O9W9EZAQmT8qPgHdV9Xqn71MnXeVCTL5cS4xYBWOJN61BicejUQkc4nyRXYZhcp6EY5mjXFzeAb4fcL02wpamgqBaQ6p6I4CIXAXMFZFAg3UmkCsixU7eY0sMWAVjiTfB5TBCnSJkB/3/akxKgUB2EZ6OoOtMTM6bcDIE0h5GJleWv2HsQcHsCNFmiYJVMJZE0w6Mci+c7cheAf0rMTWKqgPG/B8mc1u4bcl0ERkeUKHwYLxnaVuFsQ/1ICLPA/92ZDkhSJaTgTOA73h8viUAq2AsieZd4HtOUbGVmOxsgauNW4FHROQT4L/AMc6Yb0d45jjgHhG5DWPI/T6m2oAX7gIWi8jFGKVyEnAkJoFVE3CJiCzElGQtwxidn7anSH3DniJZEs1DmGxyfwEWY9JS9tSZVtUnMXmEf4I53v4pJmH5o3s8aTcfYspvvI85fbpIVf/mRRhVfRNTRvZiTErK84BTVXWlU4/6OOAgzIroIcyJ02VhHmeJgnW0s6QVjp/L/6jqQcmWxRIdu4KxWCwJwyoYi8WSMOwWyWKxJAy7grFYLAnDKhiLxZIwrIKxWCwJwyoYi8WSMKyCsVgsCeP/Ay0fHhp5Qn+EAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#还可以观察预测值与真值的散点图\n",
    "plt.figure(figsize=(4, 3))\n",
    "plt.scatter(y_train, y_train_pred_lr)\n",
    "plt.plot([-3, 3], [-3, 3], '--k')   #数据已经标准化，3倍标准差即可\n",
    "plt.axis('tight')\n",
    "plt.xlabel('True price')\n",
    "plt.ylabel('Predicted price')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is 0.828974067637878\n",
      "The r2 score of RidgeCV on train is 0.8497700029725641\n"
     ]
    }
   ],
   "source": [
    "#岭回归／L2正则\n",
    "#class sklearn.linear_model.RidgeCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, \n",
    "#                                  normalize=False, scoring=None, cv=None, gcv_mode=None, \n",
    "#                                  store_cv_values=False)\n",
    "from sklearn.linear_model import  RidgeCV\n",
    "\n",
    "#1. 设置超参数（正则参数）范围\n",
    "alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "#n_alphas = 20\n",
    "#alphas = np.logspace(-5,2,n_alphas)\n",
    "\n",
    "#2. 生成一个RidgeCV实例\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)  \n",
    "\n",
    "#3. 模型训练\n",
    "ridge.fit(X_train, y_train)    \n",
    "\n",
    "#4. 预测\n",
    "y_test_pred_ridge = ridge.predict(X_test)\n",
    "y_train_pred_ridge = ridge.predict(X_train)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print(\"The r2 score of RidgeCV on test is\", r2_score(y_test, y_test_pred_ridge))\n",
    "print(\"The r2 score of RidgeCV on train is\", r2_score(y_train, y_train_pred_ridge))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is: 1.0\n"
     ]
    }
   ],
   "source": [
    "mse_mean = np.mean(ridge.cv_values_, axis = 0)\n",
    "plt.plot(np.log10(alphas), mse_mean.reshape(len(alphas),1)) \n",
    "\n",
    "#这是为了标出最佳参数的位置，不是必须\n",
    "#plt.plot(np.log10(ridge.alpha_)*np.ones(3), [0.28, 0.29, 0.30])\n",
    "\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "\n",
    "print ('alpha is:', ridge.alpha_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>coef_lr</th>\n",
       "      <th>coef_ridge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>yr</td>\n",
       "      <td>4550.708897</td>\n",
       "      <td>1504.623924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>temp</td>\n",
       "      <td>2654.792827</td>\n",
       "      <td>1778.493414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>mnth_9</td>\n",
       "      <td>1287.992360</td>\n",
       "      <td>678.352931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>atemp</td>\n",
       "      <td>995.293778</td>\n",
       "      <td>1546.374034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>mnth_10</td>\n",
       "      <td>929.887649</td>\n",
       "      <td>84.873020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>weathersit_1</td>\n",
       "      <td>914.410909</td>\n",
       "      <td>914.843092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>season_4</td>\n",
       "      <td>830.579518</td>\n",
       "      <td>767.205317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>mnth_12</td>\n",
       "      <td>586.296405</td>\n",
       "      <td>-824.928596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>mnth_8</td>\n",
       "      <td>517.803038</td>\n",
       "      <td>205.686009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>weathersit_2</td>\n",
       "      <td>409.589079</td>\n",
       "      <td>388.865215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>mnth_11</td>\n",
       "      <td>407.138803</td>\n",
       "      <td>-725.828529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>weekday_6</td>\n",
       "      <td>238.417312</td>\n",
       "      <td>227.515740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>workingday</td>\n",
       "      <td>216.956549</td>\n",
       "      <td>212.558710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>mnth_6</td>\n",
       "      <td>189.797216</td>\n",
       "      <td>369.663154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>season_2</td>\n",
       "      <td>85.141889</td>\n",
       "      <td>110.998614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>weekday_5</td>\n",
       "      <td>77.516263</td>\n",
       "      <td>81.396550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>weekday_3</td>\n",
       "      <td>66.464787</td>\n",
       "      <td>59.857933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>weekday_4</td>\n",
       "      <td>43.650546</td>\n",
       "      <td>62.795850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>mnth_5</td>\n",
       "      <td>5.009200</td>\n",
       "      <td>387.713628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>instant</td>\n",
       "      <td>-6.919060</td>\n",
       "      <td>1.417157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>weekday_2</td>\n",
       "      <td>-31.943212</td>\n",
       "      <td>-34.140080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>season_3</td>\n",
       "      <td>-130.807162</td>\n",
       "      <td>-91.388275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>weekday_0</td>\n",
       "      <td>-191.612446</td>\n",
       "      <td>-191.851510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>weekday_1</td>\n",
       "      <td>-202.493250</td>\n",
       "      <td>-205.574484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>mnth_7</td>\n",
       "      <td>-203.137582</td>\n",
       "      <td>-242.548582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>holiday</td>\n",
       "      <td>-263.761415</td>\n",
       "      <td>-248.222941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>mnth_4</td>\n",
       "      <td>-485.714239</td>\n",
       "      <td>106.306513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mnth_3</td>\n",
       "      <td>-541.439917</td>\n",
       "      <td>298.550050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>season_1</td>\n",
       "      <td>-784.914245</td>\n",
       "      <td>-786.815656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>-1174.845790</td>\n",
       "      <td>-1087.773198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>mnth_2</td>\n",
       "      <td>-1207.111892</td>\n",
       "      <td>-143.125249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>hum</td>\n",
       "      <td>-1298.592138</td>\n",
       "      <td>-1142.397276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>weathersit_3</td>\n",
       "      <td>-1323.999988</td>\n",
       "      <td>-1303.708307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>mnth_1</td>\n",
       "      <td>-1486.521042</td>\n",
       "      <td>-194.714350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         columns      coef_lr   coef_ridge\n",
       "33            yr  4550.708897  1504.623924\n",
       "27          temp  2654.792827  1778.493414\n",
       "13        mnth_9  1287.992360   678.352931\n",
       "28         atemp   995.293778  1546.374034\n",
       "14       mnth_10   929.887649    84.873020\n",
       "17  weathersit_1   914.410909   914.843092\n",
       "4       season_4   830.579518   767.205317\n",
       "16       mnth_12   586.296405  -824.928596\n",
       "12        mnth_8   517.803038   205.686009\n",
       "18  weathersit_2   409.589079   388.865215\n",
       "15       mnth_11   407.138803  -725.828529\n",
       "26     weekday_6   238.417312   227.515740\n",
       "32    workingday   216.956549   212.558710\n",
       "10        mnth_6   189.797216   369.663154\n",
       "2       season_2    85.141889   110.998614\n",
       "25     weekday_5    77.516263    81.396550\n",
       "23     weekday_3    66.464787    59.857933\n",
       "24     weekday_4    43.650546    62.795850\n",
       "9         mnth_5     5.009200   387.713628\n",
       "0        instant    -6.919060     1.417157\n",
       "22     weekday_2   -31.943212   -34.140080\n",
       "3       season_3  -130.807162   -91.388275\n",
       "20     weekday_0  -191.612446  -191.851510\n",
       "21     weekday_1  -202.493250  -205.574484\n",
       "11        mnth_7  -203.137582  -242.548582\n",
       "31       holiday  -263.761415  -248.222941\n",
       "8         mnth_4  -485.714239   106.306513\n",
       "7         mnth_3  -541.439917   298.550050\n",
       "1       season_1  -784.914245  -786.815656\n",
       "30     windspeed -1174.845790 -1087.773198\n",
       "6         mnth_2 -1207.111892  -143.125249\n",
       "29           hum -1298.592138 -1142.397276\n",
       "19  weathersit_3 -1323.999988 -1303.708307\n",
       "5         mnth_1 -1486.521042  -194.714350"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(feat_names), \"coef_lr\":list((lr.coef_.T)), \"coef_ridge\":list((ridge.coef_.T))})\n",
    "fs.sort_values(by=['coef_lr'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LassoCV on test is 0.47062572154686677\n",
      "The r2 score of LassoCV on train is 0.4427527534180158\n"
     ]
    }
   ],
   "source": [
    "#### Lasso／L1正则\n",
    "# class sklearn.linear_model.LassoCV(eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, \n",
    "#                                    normalize=False, precompute=’auto’, max_iter=1000, \n",
    "#                                    tol=0.0001, copy_X=True, cv=None, verbose=False, n_jobs=1,\n",
    "#                                    positive=False, random_state=None, selection=’cyclic’)\n",
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "#1. 设置超参数搜索范围\n",
    "#alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "\n",
    "#2. 生成学习器实例\n",
    "#lasso = LassoCV(alphas=alphas)\n",
    "\n",
    "#1. 设置超参数搜索范围\n",
    "#Lasso可以自动确定最大的alpha，所以另一种设置alpha的方式是设置最小的alpha值（eps） 和 超参数的数目（n_alphas），\n",
    "#然后LassoCV对最小值和最大值之间在log域上均匀取值n_alphas个\n",
    "# np.logspace(np.log10(alpha_max * eps), np.log10(alpha_max),num=n_alphas)[::-1]\n",
    "\n",
    "#2 生成LassoCV实例（默认超参数搜索范围）\n",
    "lasso = LassoCV()  \n",
    "\n",
    "#3. 训练（内含CV）\n",
    "lasso.fit(X_train, y_train)  \n",
    "\n",
    "#4. 测试\n",
    "y_test_pred_lasso = lasso.predict(X_test)\n",
    "y_train_pred_lasso = lasso.predict(X_train)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print(\"The r2 score of LassoCV on test is\", r2_score(y_test, y_test_pred_lasso))\n",
    "print(\"The r2 score of LassoCV on train is\", r2_score(y_train, y_train_pred_lasso))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is: 256.4697701374553\n"
     ]
    }
   ],
   "source": [
    "mses = np.mean(lasso.mse_path_, axis = 1)\n",
    "plt.plot(np.log10(lasso.alphas_), mses) \n",
    "#plt.plot(np.log10(lasso.alphas_)*np.ones(3), [0.3, 0.4, 1.0])\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()    \n",
    "            \n",
    "print ('alpha is:', lasso.alpha_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>coef_lr</th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>coef_lasso</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>yr</td>\n",
       "      <td>4550.708897</td>\n",
       "      <td>1504.623924</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>temp</td>\n",
       "      <td>2654.792827</td>\n",
       "      <td>1778.493414</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>mnth_9</td>\n",
       "      <td>1287.992360</td>\n",
       "      <td>678.352931</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>atemp</td>\n",
       "      <td>995.293778</td>\n",
       "      <td>1546.374034</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>mnth_10</td>\n",
       "      <td>929.887649</td>\n",
       "      <td>84.873020</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>weathersit_1</td>\n",
       "      <td>914.410909</td>\n",
       "      <td>914.843092</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>season_4</td>\n",
       "      <td>830.579518</td>\n",
       "      <td>767.205317</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>mnth_12</td>\n",
       "      <td>586.296405</td>\n",
       "      <td>-824.928596</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>mnth_8</td>\n",
       "      <td>517.803038</td>\n",
       "      <td>205.686009</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>weathersit_2</td>\n",
       "      <td>409.589079</td>\n",
       "      <td>388.865215</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>mnth_11</td>\n",
       "      <td>407.138803</td>\n",
       "      <td>-725.828529</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>weekday_6</td>\n",
       "      <td>238.417312</td>\n",
       "      <td>227.515740</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>workingday</td>\n",
       "      <td>216.956549</td>\n",
       "      <td>212.558710</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>mnth_6</td>\n",
       "      <td>189.797216</td>\n",
       "      <td>369.663154</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>season_2</td>\n",
       "      <td>85.141889</td>\n",
       "      <td>110.998614</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>weekday_5</td>\n",
       "      <td>77.516263</td>\n",
       "      <td>81.396550</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>weekday_3</td>\n",
       "      <td>66.464787</td>\n",
       "      <td>59.857933</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>weekday_4</td>\n",
       "      <td>43.650546</td>\n",
       "      <td>62.795850</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>mnth_5</td>\n",
       "      <td>5.009200</td>\n",
       "      <td>387.713628</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>instant</td>\n",
       "      <td>-6.919060</td>\n",
       "      <td>1.417157</td>\n",
       "      <td>5.456671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>weekday_2</td>\n",
       "      <td>-31.943212</td>\n",
       "      <td>-34.140080</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>season_3</td>\n",
       "      <td>-130.807162</td>\n",
       "      <td>-91.388275</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>weekday_0</td>\n",
       "      <td>-191.612446</td>\n",
       "      <td>-191.851510</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>weekday_1</td>\n",
       "      <td>-202.493250</td>\n",
       "      <td>-205.574484</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>mnth_7</td>\n",
       "      <td>-203.137582</td>\n",
       "      <td>-242.548582</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>holiday</td>\n",
       "      <td>-263.761415</td>\n",
       "      <td>-248.222941</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>mnth_4</td>\n",
       "      <td>-485.714239</td>\n",
       "      <td>106.306513</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mnth_3</td>\n",
       "      <td>-541.439917</td>\n",
       "      <td>298.550050</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>season_1</td>\n",
       "      <td>-784.914245</td>\n",
       "      <td>-786.815656</td>\n",
       "      <td>-341.190505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>-1174.845790</td>\n",
       "      <td>-1087.773198</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>mnth_2</td>\n",
       "      <td>-1207.111892</td>\n",
       "      <td>-143.125249</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>hum</td>\n",
       "      <td>-1298.592138</td>\n",
       "      <td>-1142.397276</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>weathersit_3</td>\n",
       "      <td>-1323.999988</td>\n",
       "      <td>-1303.708307</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>mnth_1</td>\n",
       "      <td>-1486.521042</td>\n",
       "      <td>-194.714350</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         columns      coef_lr   coef_ridge  coef_lasso\n",
       "33            yr  4550.708897  1504.623924    0.000000\n",
       "27          temp  2654.792827  1778.493414    0.000000\n",
       "13        mnth_9  1287.992360   678.352931    0.000000\n",
       "28         atemp   995.293778  1546.374034    0.000000\n",
       "14       mnth_10   929.887649    84.873020    0.000000\n",
       "17  weathersit_1   914.410909   914.843092    0.000000\n",
       "4       season_4   830.579518   767.205317   -0.000000\n",
       "16       mnth_12   586.296405  -824.928596   -0.000000\n",
       "12        mnth_8   517.803038   205.686009    0.000000\n",
       "18  weathersit_2   409.589079   388.865215   -0.000000\n",
       "15       mnth_11   407.138803  -725.828529   -0.000000\n",
       "26     weekday_6   238.417312   227.515740   -0.000000\n",
       "32    workingday   216.956549   212.558710    0.000000\n",
       "10        mnth_6   189.797216   369.663154    0.000000\n",
       "2       season_2    85.141889   110.998614    0.000000\n",
       "25     weekday_5    77.516263    81.396550    0.000000\n",
       "23     weekday_3    66.464787    59.857933    0.000000\n",
       "24     weekday_4    43.650546    62.795850    0.000000\n",
       "9         mnth_5     5.009200   387.713628    0.000000\n",
       "0        instant    -6.919060     1.417157    5.456671\n",
       "22     weekday_2   -31.943212   -34.140080    0.000000\n",
       "3       season_3  -130.807162   -91.388275    0.000000\n",
       "20     weekday_0  -191.612446  -191.851510   -0.000000\n",
       "21     weekday_1  -202.493250  -205.574484   -0.000000\n",
       "11        mnth_7  -203.137582  -242.548582    0.000000\n",
       "31       holiday  -263.761415  -248.222941   -0.000000\n",
       "8         mnth_4  -485.714239   106.306513    0.000000\n",
       "7         mnth_3  -541.439917   298.550050    0.000000\n",
       "1       season_1  -784.914245  -786.815656 -341.190505\n",
       "30     windspeed -1174.845790 -1087.773198   -0.000000\n",
       "6         mnth_2 -1207.111892  -143.125249   -0.000000\n",
       "29           hum -1298.592138 -1142.397276   -0.000000\n",
       "19  weathersit_3 -1323.999988 -1303.708307   -0.000000\n",
       "5         mnth_1 -1486.521042  -194.714350   -0.000000"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(feat_names), \"coef_lr\":list((lr.coef_.T)), \"coef_ridge\":list((ridge.coef_.T)), \"coef_lasso\":list((lasso.coef_.T))})\n",
    "fs.sort_values(by=['coef_lr'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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